C. Andriotis
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1
Building Automation and Control Systems (BACS) enhance energy efficiency in office buildings but often leave occupants dissatisfied, especially in shared offices with diverse indoor environmental preferences. Personal Comfort Models (PCM) capture individual preferences, yet these models can be difficult to apply in shared environments. This study examines whether a generalized personalized modeling would be necessary, or instead aggregated thermal preferences adequately represent individual needs in shared spaces. By mining the ASHRAE Global Thermal Comfort Database II, we analyzed 11 naturally ventilated office buildings spanning four climate zones, each providing 187 to 568 data points of subjective thermal preferences and concurrent environmental measurements. To capture uncertainties in occupant feedback and monitoring, we developed a Bayesian multinomial logistic framework. We compare an aggregated model (all responses together) with a hierarchical (partial-pooling) model having occupant-specific intercepts, using the No‑U‑Turn Sampler for Hamiltonian Monte Carlo. The Widely Applicable Bayesian Information Criterion confirms that hierarchical approach outperforms the aggregated model in every case. In four of the eleven studies, aggregated air temperature ranges satisfied occupants’ demands within a 95% posterior credible interval. Moreover, in most buildings, these temperature ranges aligned well with the majority of occupant preferences, suggesting that prior knowledge of building conditions or occupant variability might reduce the need for highly customized thermal environment. Future research should explore these findings in conditioned buildings and across other indoor environmental quality domains, where greater variability could exist.
...
Building Automation and Control Systems (BACS) enhance energy efficiency in office buildings but often leave occupants dissatisfied, especially in shared offices with diverse indoor environmental preferences. Personal Comfort Models (PCM) capture individual preferences, yet these models can be difficult to apply in shared environments. This study examines whether a generalized personalized modeling would be necessary, or instead aggregated thermal preferences adequately represent individual needs in shared spaces. By mining the ASHRAE Global Thermal Comfort Database II, we analyzed 11 naturally ventilated office buildings spanning four climate zones, each providing 187 to 568 data points of subjective thermal preferences and concurrent environmental measurements. To capture uncertainties in occupant feedback and monitoring, we developed a Bayesian multinomial logistic framework. We compare an aggregated model (all responses together) with a hierarchical (partial-pooling) model having occupant-specific intercepts, using the No‑U‑Turn Sampler for Hamiltonian Monte Carlo. The Widely Applicable Bayesian Information Criterion confirms that hierarchical approach outperforms the aggregated model in every case. In four of the eleven studies, aggregated air temperature ranges satisfied occupants’ demands within a 95% posterior credible interval. Moreover, in most buildings, these temperature ranges aligned well with the majority of occupant preferences, suggesting that prior knowledge of building conditions or occupant variability might reduce the need for highly customized thermal environment. Future research should explore these findings in conditioned buildings and across other indoor environmental quality domains, where greater variability could exist.
Life-cycle fragility analysis of aging reinforced concrete bridges
A dynamic Bayesian network approach
Reinforced concrete bridges are predominant structural systems in transportation infrastructure. Their exposure to chronic and sudden stressors, such as corrosion and earthquakes, make them prone to risks with severe socioeconomic consequences. While time-dependent single-component seismic fragility formulations have advanced the frontier of life-cycle probabilistic risk assessment, state-dependent multi-component representations of damage and deterioration, paramount for structural integrity management, still lack a systematic probabilistic framework. This paper develops a novel dynamic Bayesian network to evaluate the life-cycle fragility functions of aging bridges, encapsulating the impacts of corrosion and seismic phenomena over time. The network establishes Markovian transitions among deterioration states for various bridge components integrating chloride diffusion and corrosion propagation models with non-stationary Gamma processes. A methodology for deriving and state-dependent fragility at the component and system levels depending on several deterioration scenarios is presented. Our framework is exemplified in an archetypical 4-span bridge, demonstrating the longitudinal effects of corrosion on the system's seismic fragility for splash and atmospheric conditions. Insights from the multi-component analysis highlight the capabilities in understanding the pathologies and evolving mechanical interactions among components. The adaptability in accommodating on-site observations and advanced decision-making algorithms is discussed, demonstrating the suitability of the framework for applications requiring flexible and updatable virtual environments.
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Reinforced concrete bridges are predominant structural systems in transportation infrastructure. Their exposure to chronic and sudden stressors, such as corrosion and earthquakes, make them prone to risks with severe socioeconomic consequences. While time-dependent single-component seismic fragility formulations have advanced the frontier of life-cycle probabilistic risk assessment, state-dependent multi-component representations of damage and deterioration, paramount for structural integrity management, still lack a systematic probabilistic framework. This paper develops a novel dynamic Bayesian network to evaluate the life-cycle fragility functions of aging bridges, encapsulating the impacts of corrosion and seismic phenomena over time. The network establishes Markovian transitions among deterioration states for various bridge components integrating chloride diffusion and corrosion propagation models with non-stationary Gamma processes. A methodology for deriving and state-dependent fragility at the component and system levels depending on several deterioration scenarios is presented. Our framework is exemplified in an archetypical 4-span bridge, demonstrating the longitudinal effects of corrosion on the system's seismic fragility for splash and atmospheric conditions. Insights from the multi-component analysis highlight the capabilities in understanding the pathologies and evolving mechanical interactions among components. The adaptability in accommodating on-site observations and advanced decision-making algorithms is discussed, demonstrating the suitability of the framework for applications requiring flexible and updatable virtual environments.
Building automation and control systems (BACS) are central to energy performance and occupant comfort in non-residential buildings. Comfort is inherently multi-domain, including thermal, visual, acoustic, and air quality requirements. Multi-domain BACS involves frequent trade-offs across domains when conflicting control actions arise, such as providing glare control versus daylight availability. Yet existing occupant-centric control research treats building services in isolation, and prior multi-domain comfort reviews rarely examine how multi-domain demands are integrated into BACS decision logic across services. We conducted a systematic review of 43 studies to examine how multi-domain occupant demands are represented and operationalized in BACS. Across the evidence base, thermal comfort is universal, while visual and air quality are frequently included. Acoustics is rarely addressed due controllability constraints. Most studies remain unimodal in their demand representation, even when multiple domains are in scope. Integrated BACS implementations are therefore largely built on within-domain formulations. Multimodal demand models that encode cross-domain and combined effects are uncommon and are rarely implemented in integrated BACS. Rule-based strategies dominate multi-domain controllers. Optimization-based and learning-based controllers are also used, but they often rely on fixed weights or reward terms that make trade-offs difficult to interpret. In addition, actuator choice is rarely made explicit when multiple services can achieve the same target state. Future research should benchmark unimodal and multimodal demand formulations under comparable control contexts, extend bottom-up multimodal models beyond thermal and air quality into integrated BACS, especially for façade control, and develop transparent, preference-aware policy designs that make priorities and service actions understandable.
...
Building automation and control systems (BACS) are central to energy performance and occupant comfort in non-residential buildings. Comfort is inherently multi-domain, including thermal, visual, acoustic, and air quality requirements. Multi-domain BACS involves frequent trade-offs across domains when conflicting control actions arise, such as providing glare control versus daylight availability. Yet existing occupant-centric control research treats building services in isolation, and prior multi-domain comfort reviews rarely examine how multi-domain demands are integrated into BACS decision logic across services. We conducted a systematic review of 43 studies to examine how multi-domain occupant demands are represented and operationalized in BACS. Across the evidence base, thermal comfort is universal, while visual and air quality are frequently included. Acoustics is rarely addressed due controllability constraints. Most studies remain unimodal in their demand representation, even when multiple domains are in scope. Integrated BACS implementations are therefore largely built on within-domain formulations. Multimodal demand models that encode cross-domain and combined effects are uncommon and are rarely implemented in integrated BACS. Rule-based strategies dominate multi-domain controllers. Optimization-based and learning-based controllers are also used, but they often rely on fixed weights or reward terms that make trade-offs difficult to interpret. In addition, actuator choice is rarely made explicit when multiple services can achieve the same target state. Future research should benchmark unimodal and multimodal demand formulations under comparable control contexts, extend bottom-up multimodal models beyond thermal and air quality into integrated BACS, especially for façade control, and develop transparent, preference-aware policy designs that make priorities and service actions understandable.
Predicting building operational energy under material degradation and climate uncertainty
A sensitivity analysis
Conference paper
(2025)
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A.M. Koniari, C. Andriotis, S. Bianchi, Pablo G. Morato, S. Khademi, M. Overend
Building energy prediction models expedite performance assessment and assist in decision making, from early-stage design to retrofit planning at single- or multi-building scales. However, the number of parameters involved in the energy performance evaluation often impede the prediction process requiring the assimilation of high-dimensional, uncertain input. This is compounded further at multi-building scale e.g. urban energy modelling, due to the increased complexity of evaluating diverse building geometries. While single-building sensitivity and uncertainty analysis is well-established for identifying the most influential input parameters and evaluate the uncertainty effects on energy demand, these are hard to generalize at multi-building scale which remains relatively unexplored. The present study advances existing research by applying a variance-based sensitivity analysis to assess the impact of varying (i) building façade layout, (ii) envelope thermal properties, (iii) envelope air tightness and (iv) building occupancy. The analysis is conducted for multiple buildings under two future climate variations, while also considering the degradation of material thermal properties. The latter is derived from known deterioration models for single-building uncertainty propagation, relying on experimental and simulated data. The approach is applied to a temperate oceanic climate with particular focus on the Dutch building stock, including a sample of buildings with diverse geometric characteristics in Rotterdam. First-order Sobol indices are computed to evaluate the impact with respect to the heating, cooling and total energy demand. Our findings indicate that infiltration is the most influential factor for heating energy demand, whereas cooling is mostly affected by the envelope thermal properties and, particularly, window solar heat gain coefficient. Common patterns regarding the impact of insulation across different envelope components can be identified among buildings with similar orientation and compactness ratio indicating the importance of considering these geometric properties in retrofit decision-making workflows.
...
Building energy prediction models expedite performance assessment and assist in decision making, from early-stage design to retrofit planning at single- or multi-building scales. However, the number of parameters involved in the energy performance evaluation often impede the prediction process requiring the assimilation of high-dimensional, uncertain input. This is compounded further at multi-building scale e.g. urban energy modelling, due to the increased complexity of evaluating diverse building geometries. While single-building sensitivity and uncertainty analysis is well-established for identifying the most influential input parameters and evaluate the uncertainty effects on energy demand, these are hard to generalize at multi-building scale which remains relatively unexplored. The present study advances existing research by applying a variance-based sensitivity analysis to assess the impact of varying (i) building façade layout, (ii) envelope thermal properties, (iii) envelope air tightness and (iv) building occupancy. The analysis is conducted for multiple buildings under two future climate variations, while also considering the degradation of material thermal properties. The latter is derived from known deterioration models for single-building uncertainty propagation, relying on experimental and simulated data. The approach is applied to a temperate oceanic climate with particular focus on the Dutch building stock, including a sample of buildings with diverse geometric characteristics in Rotterdam. First-order Sobol indices are computed to evaluate the impact with respect to the heating, cooling and total energy demand. Our findings indicate that infiltration is the most influential factor for heating energy demand, whereas cooling is mostly affected by the envelope thermal properties and, particularly, window solar heat gain coefficient. Common patterns regarding the impact of insulation across different envelope components can be identified among buildings with similar orientation and compactness ratio indicating the importance of considering these geometric properties in retrofit decision-making workflows.
Conference paper
(2025)
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P. Martinez-Alcaraz, P. de la Barra, C. P. Andriotis, Y. Wang, A. Luna-Navarro
It is a challenge for traditional building control systems to meet occupants’ needs in shared spaces due to the lack of understanding of individual occupant thermal preferences. This is a barrier to balancing energy efficiency and indoor environmental quality (IEQ). Advanced statistical learning methods offer new solutions towards more energy-efficient and user-centric control logics. In this work, a control logic is proposed to optimise the heating, ventilation and air conditioning (HVAC) operation based on thermal comfort archetype preferences, leveraging the ASHRAE Global Thermal Comfort Database II in conjunction with energy simulations. First, we apply the k-means clustering algorithm to categorize occupants into different archetypes regarding their common feedback on the thermal environment. Then, we fit a Bayesian logistic regression model to predict the thermal comfort preferences of different archetypes based on IEQ data. Finally, we identify two occupant-centric control logics to optimize HVAC operation to meet occupants’ requirements: (i) considering a unified response of thermal comfort in the space, and (ii) ensuring the dynamic optimal setpoint when conflicting occupant archetypes are present. Having compared this control logic with a common rule-based logic, our results demonstrate the potential of occupant-centric controls and the importance of multi-objective metrics in accounting for energy efficiency.
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It is a challenge for traditional building control systems to meet occupants’ needs in shared spaces due to the lack of understanding of individual occupant thermal preferences. This is a barrier to balancing energy efficiency and indoor environmental quality (IEQ). Advanced statistical learning methods offer new solutions towards more energy-efficient and user-centric control logics. In this work, a control logic is proposed to optimise the heating, ventilation and air conditioning (HVAC) operation based on thermal comfort archetype preferences, leveraging the ASHRAE Global Thermal Comfort Database II in conjunction with energy simulations. First, we apply the k-means clustering algorithm to categorize occupants into different archetypes regarding their common feedback on the thermal environment. Then, we fit a Bayesian logistic regression model to predict the thermal comfort preferences of different archetypes based on IEQ data. Finally, we identify two occupant-centric control logics to optimize HVAC operation to meet occupants’ requirements: (i) considering a unified response of thermal comfort in the space, and (ii) ensuring the dynamic optimal setpoint when conflicting occupant archetypes are present. Having compared this control logic with a common rule-based logic, our results demonstrate the potential of occupant-centric controls and the importance of multi-objective metrics in accounting for energy efficiency.
Journal article
(2025)
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Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers, Charalampos Andriotis, Yingqian Zhang, Zaharah Bukhsh
In this paper, we introduce multi-objective deep centralized multi-agent actor-critic (MO-DCMAC), a multi-objective reinforcement learning method for infrastructural maintenance optimization, an area traditionally dominated by single-objective reinforcement learning (RL) approaches. Previous single-objective RL methods combine multiple objectives, such as probability of collapse and cost, into a singular reward signal through reward-shaping. In contrast, MO-DCMAC can optimize a policy for multiple objectives directly, even when the utility function is nonlinear. We evaluated MO-DCMAC using two utility functions, which use probability of collapse and cost as input. The first utility function is the threshold utility, in which MO-DCMAC should minimize cost so that the probability of collapse is never above the threshold. The second is based on the failure mode, effects, and criticality analysis methodology used by asset managers to assess maintenance plans. We evaluated MO-DCMAC, with both utility functions, in multiple maintenance environments, including ones based on a case study of the historical quay walls of Amsterdam. The performance of MO-DCMAC was compared against multiple rule-based policies based on heuristics currently used for constructing maintenance plans. Our results demonstrate that MO-DCMAC outperforms traditional rule-based policies across various environments and utility functions.
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In this paper, we introduce multi-objective deep centralized multi-agent actor-critic (MO-DCMAC), a multi-objective reinforcement learning method for infrastructural maintenance optimization, an area traditionally dominated by single-objective reinforcement learning (RL) approaches. Previous single-objective RL methods combine multiple objectives, such as probability of collapse and cost, into a singular reward signal through reward-shaping. In contrast, MO-DCMAC can optimize a policy for multiple objectives directly, even when the utility function is nonlinear. We evaluated MO-DCMAC using two utility functions, which use probability of collapse and cost as input. The first utility function is the threshold utility, in which MO-DCMAC should minimize cost so that the probability of collapse is never above the threshold. The second is based on the failure mode, effects, and criticality analysis methodology used by asset managers to assess maintenance plans. We evaluated MO-DCMAC, with both utility functions, in multiple maintenance environments, including ones based on a case study of the historical quay walls of Amsterdam. The performance of MO-DCMAC was compared against multiple rule-based policies based on heuristics currently used for constructing maintenance plans. Our results demonstrate that MO-DCMAC outperforms traditional rule-based policies across various environments and utility functions.
This paper presents ongoing research that aims to develop a closed-loop and real-time error
detection and correction system in 3D clay printing (3DCP) using computer vision and machine
learning. [...] ...
detection and correction system in 3D clay printing (3DCP) using computer vision and machine
learning. [...] ...
This paper presents ongoing research that aims to develop a closed-loop and real-time error
detection and correction system in 3D clay printing (3DCP) using computer vision and machine
learning. [...]
detection and correction system in 3D clay printing (3DCP) using computer vision and machine
learning. [...]
Long-term inspection and maintenance (I&M) planning, a multi-stage stochastic optimization problem, can be efficiently formulated as a partially observable Markov decision process (POMDP). However, within this context, single-agent approaches do not scale well for large multi-component systems since the joint state, action and observation spaces grow exponentially with the number of components. To alleviate this curse of dimensionality, cooperative decentralized approaches, known as decentralized POMDPs, are often adopted and solved using multi-agent deep reinforcement learning (MADRL) algorithms. This paper examines the centralization vs. decentralization performance of MADRL formulations in I&M planning of multi-component systems. Towards this, we set up a comprehensive computational experimental program focused on k-out-of-n system configurations, a common and broadly applicable archetype of deteriorating engineering systems, to highlight the manifestations of MADRL strengths and pathologies when optimizing global returns under varying decentralization relaxations.
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Long-term inspection and maintenance (I&M) planning, a multi-stage stochastic optimization problem, can be efficiently formulated as a partially observable Markov decision process (POMDP). However, within this context, single-agent approaches do not scale well for large multi-component systems since the joint state, action and observation spaces grow exponentially with the number of components. To alleviate this curse of dimensionality, cooperative decentralized approaches, known as decentralized POMDPs, are often adopted and solved using multi-agent deep reinforcement learning (MADRL) algorithms. This paper examines the centralization vs. decentralization performance of MADRL formulations in I&M planning of multi-component systems. Towards this, we set up a comprehensive computational experimental program focused on k-out-of-n system configurations, a common and broadly applicable archetype of deteriorating engineering systems, to highlight the manifestations of MADRL strengths and pathologies when optimizing global returns under varying decentralization relaxations.
More with less
Topology optimization strategies for structural glass design
Journal article
(2025)
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Jackson L. Jewett, Anna Maria Koniari, Charalampos P. Andriotis, Faidra Oikonomopoulou, Telesilla Bristogianni, Josephine V. Carstensen
Advances in structural glass have enabled a new paradigm in expressive and transparent architecture. Cast glass can further extend the possibilities of structural glass by allowing for more complex and sophisticated shapes than the current planar geometries of structural float glass. However, the use of cast glass is currently limited because of the lengthy annealing process, making massive component sizes impractical to fabricate. Topology optimization (TO) has been proposed as a solution to this problem, as it is known to generate structurally efficient designs with a low volume of material. If tailored appropriately, TO can reduce component sizes and thereby diminish the total annealing time needed, while intelligently placing material in the areas where it will be utilized most effectively. For TO of glass to be successful, algorithms must properly capture glass’s specific material behavior. This research proposes a suite of TO algorithmic frameworks that design specifically for structural glass. These algorithms are demonstrated in a 2D design space, and the resulting geometries are fabricated using cut float glass and tested for experimental comparison on a 4-point bending load case. The results of these experiments provide valuable insights into the development of TO for structural glass, and help inform future research in TO of large-scale cast glass structures.
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Advances in structural glass have enabled a new paradigm in expressive and transparent architecture. Cast glass can further extend the possibilities of structural glass by allowing for more complex and sophisticated shapes than the current planar geometries of structural float glass. However, the use of cast glass is currently limited because of the lengthy annealing process, making massive component sizes impractical to fabricate. Topology optimization (TO) has been proposed as a solution to this problem, as it is known to generate structurally efficient designs with a low volume of material. If tailored appropriately, TO can reduce component sizes and thereby diminish the total annealing time needed, while intelligently placing material in the areas where it will be utilized most effectively. For TO of glass to be successful, algorithms must properly capture glass’s specific material behavior. This research proposes a suite of TO algorithmic frameworks that design specifically for structural glass. These algorithms are demonstrated in a 2D design space, and the resulting geometries are fabricated using cut float glass and tested for experimental comparison on a 4-point bending load case. The results of these experiments provide valuable insights into the development of TO for structural glass, and help inform future research in TO of large-scale cast glass structures.
Data-driven prediction of infrastructure aging is challenging due to the complex stochastic nature of degradation effects and the ill-documented historical records. Degradation modeling is, however, crucial for predictive maintenance that is key for infrastructure integrity. This study presents a multi-attribute, data-driven approach for modelling stochastic degradation and maintenance effects of roads, mining an extensive database of geo-located historical inspection and maintenance records from the municipality of Amsterdam. Inspection data track pavement conditions at irregular intervals across ten discrete states per road segment, following the Dutch CROW 146 protocol. Damage severity and extent for eight damage modes is captured, i.e., for transverse unevenness, irregularities, ravelling, edge damage, crack formation, joint filling, joint width, and settling. The maintenance dataset includes >25k minor interventions across 17k road segments, indicating repair requirements, and 200+ major maintenance projects, covering 21k segments where interventions are planned, all without verifying completion. This complicates accurate modelling of natural degradation as it is confounded by maintenance effects. To address the issue of irregular inspections, degradation is first modelled as a continuous-time Markov chain. Thereby, transition rates are estimated, which are then converted to discrete-time Markov chain transition probability matrices to eventually support regular maintenance planning. We further learn the effects of minor and major maintenance activities, as defined and recorded in the database. Based on the estimated degradation transitions, pre-maintenance and post-maintenance state distributions are estimated. Instantaneous maintenance transition matrices are computed by minimizing the cross-entropy between the pre-maintenance state after the intervention and the post-maintenance state. The model allows for a multi-attribute approach, segmenting roads based on construction material (e.g., asphalt, tiled pavement) and traffic loads (e.g., residential, commercial/pedestrian). The approach is exemplified for tiled pavements for a section of the road network of Amsterdam, where the effects of minor and major maintenance are ablated for long-term predictions. Although applied to Amsterdam, this method is relevant to any infrastructure system with discrete state datasets, providing a foundation for data-driven decision-making in infrastructure management.
...
Data-driven prediction of infrastructure aging is challenging due to the complex stochastic nature of degradation effects and the ill-documented historical records. Degradation modeling is, however, crucial for predictive maintenance that is key for infrastructure integrity. This study presents a multi-attribute, data-driven approach for modelling stochastic degradation and maintenance effects of roads, mining an extensive database of geo-located historical inspection and maintenance records from the municipality of Amsterdam. Inspection data track pavement conditions at irregular intervals across ten discrete states per road segment, following the Dutch CROW 146 protocol. Damage severity and extent for eight damage modes is captured, i.e., for transverse unevenness, irregularities, ravelling, edge damage, crack formation, joint filling, joint width, and settling. The maintenance dataset includes >25k minor interventions across 17k road segments, indicating repair requirements, and 200+ major maintenance projects, covering 21k segments where interventions are planned, all without verifying completion. This complicates accurate modelling of natural degradation as it is confounded by maintenance effects. To address the issue of irregular inspections, degradation is first modelled as a continuous-time Markov chain. Thereby, transition rates are estimated, which are then converted to discrete-time Markov chain transition probability matrices to eventually support regular maintenance planning. We further learn the effects of minor and major maintenance activities, as defined and recorded in the database. Based on the estimated degradation transitions, pre-maintenance and post-maintenance state distributions are estimated. Instantaneous maintenance transition matrices are computed by minimizing the cross-entropy between the pre-maintenance state after the intervention and the post-maintenance state. The model allows for a multi-attribute approach, segmenting roads based on construction material (e.g., asphalt, tiled pavement) and traffic loads (e.g., residential, commercial/pedestrian). The approach is exemplified for tiled pavements for a section of the road network of Amsterdam, where the effects of minor and major maintenance are ablated for long-term predictions. Although applied to Amsterdam, this method is relevant to any infrastructure system with discrete state datasets, providing a foundation for data-driven decision-making in infrastructure management.
Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans.
...
Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans.
Assessing life-cycle seismic safety of aging reinforced concrete bridges is a challenging engineering task. Deterioration phenomena reduce structural capacity, exacerbating poor design choices that are typical of old bridges, while also being characterized by major uncertainties. Management of engineering systems in highly uncertain environments can be efficiently addressed through Markov decision processes, which rely on dynamic Bayesian networks to model the deteriorating system’s life-cycle. However, there is still a gap in developing virtual environments that can seamlessly fit in such advanced algorithmic decision-making frameworks, especially under life-cycle seismic behavior considerations. In this study, we develop a dynamic Bayesian network capable of incorporating disparate uncertainties related to chloride-induced corrosion and seismic action, aiming at providing fragility curves over the bridge service life. The framework is applied to a prototype bridge encapsulating key risk-prone features. Using a multi-component approach, the developed network provides valuable insights into the fragility evaluation of both the system and individual components. Markovian transitions among component deterioration states are computed by combining corrosion initiation and propagation models with non-stationary Gamma processes. Subsequently, state-dependent fragilities are obtained through probabilistic seismic assessment based on non-linear dynamic analyses and multinomial logistic regression. Results show that the approach sheds light on the risk interplay mechanisms between components and the system, and on how different corrosion scenarios affect the system fragility. Discussion is finally provided on how these risk considerations can be interpreted for decision-making, allowing for better repair and retrofit strategies.
...
Assessing life-cycle seismic safety of aging reinforced concrete bridges is a challenging engineering task. Deterioration phenomena reduce structural capacity, exacerbating poor design choices that are typical of old bridges, while also being characterized by major uncertainties. Management of engineering systems in highly uncertain environments can be efficiently addressed through Markov decision processes, which rely on dynamic Bayesian networks to model the deteriorating system’s life-cycle. However, there is still a gap in developing virtual environments that can seamlessly fit in such advanced algorithmic decision-making frameworks, especially under life-cycle seismic behavior considerations. In this study, we develop a dynamic Bayesian network capable of incorporating disparate uncertainties related to chloride-induced corrosion and seismic action, aiming at providing fragility curves over the bridge service life. The framework is applied to a prototype bridge encapsulating key risk-prone features. Using a multi-component approach, the developed network provides valuable insights into the fragility evaluation of both the system and individual components. Markovian transitions among component deterioration states are computed by combining corrosion initiation and propagation models with non-stationary Gamma processes. Subsequently, state-dependent fragilities are obtained through probabilistic seismic assessment based on non-linear dynamic analyses and multinomial logistic regression. Results show that the approach sheds light on the risk interplay mechanisms between components and the system, and on how different corrosion scenarios affect the system fragility. Discussion is finally provided on how these risk considerations can be interpreted for decision-making, allowing for better repair and retrofit strategies.
Multi-criteria design methods in façade engineering
State-of-the-art and future trends
Façade engineering is facing an era of extraordinary challenge to meet the surge in demand for buildings that are environmentally sustainable and enhance occupant wellbeing. Facades, also known as building envelopes, play a major role in the resource-efficiency of buildings and the quality of its indoor environment. Consequently, the development of effective design approaches is crucial for generating appropriate façade solutions. Façade design is complex and multi-disciplinary involving several and oftentimes conflicting performance criteria. Systematic and holistic design procedures are, therefore, required to achieve optimal trade-offs. Over the last decades, researchers in this field have used computational tools and power to address this challenging problem within the context of multi-criteria design approaches. This paper reviews the existing research in this field, and presents the state-of-the-art review from simple to advanced decision-making procedures currently used at the early design stages, where decisions have a disproportionally large impact on the façade performance. The paper provides a complete description of the design variables and objectives typically involved. Alternative multi-criteria design methodologies regarding discrete decisions and automated optimization are reviewed, each with salient pros/cons, and overall conclusions are drawn. Finally, the paper discusses ongoing trends and research needs, namely, the development of uncertainty-based procedures to enable more informed decision-making; the inclusion of structural/seismic safety considerations in the design process to achieve higher socio-economic benefits; the integration of smart building information modeling and processing technologies to facilitate smarter design decisions; and the adoption of integrated design approaches to promote climate-adaptive solutions that enhance resilience.
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Façade engineering is facing an era of extraordinary challenge to meet the surge in demand for buildings that are environmentally sustainable and enhance occupant wellbeing. Facades, also known as building envelopes, play a major role in the resource-efficiency of buildings and the quality of its indoor environment. Consequently, the development of effective design approaches is crucial for generating appropriate façade solutions. Façade design is complex and multi-disciplinary involving several and oftentimes conflicting performance criteria. Systematic and holistic design procedures are, therefore, required to achieve optimal trade-offs. Over the last decades, researchers in this field have used computational tools and power to address this challenging problem within the context of multi-criteria design approaches. This paper reviews the existing research in this field, and presents the state-of-the-art review from simple to advanced decision-making procedures currently used at the early design stages, where decisions have a disproportionally large impact on the façade performance. The paper provides a complete description of the design variables and objectives typically involved. Alternative multi-criteria design methodologies regarding discrete decisions and automated optimization are reviewed, each with salient pros/cons, and overall conclusions are drawn. Finally, the paper discusses ongoing trends and research needs, namely, the development of uncertainty-based procedures to enable more informed decision-making; the inclusion of structural/seismic safety considerations in the design process to achieve higher socio-economic benefits; the integration of smart building information modeling and processing technologies to facilitate smarter design decisions; and the adoption of integrated design approaches to promote climate-adaptive solutions that enhance resilience.
Generative Artificial Intelligence (AI) promises to make a vast impact across disciplines, including transforming the architectural design process by autonomously generating full building geometries. One form of generative deep learning that has been used to create 2D and 3D representations of objects is Generative Adversarial Networks (GANs). Existing literature, however, has limited applications that utilize 3D data for building geometry generation, with previous studies focused on low-scale 3D geometries suitable for objects such as chairs or cars. This paper develops a new GAN architecture to produce high-resolution feasible building geometry. The training dataset used is a selection of 3D models of single-family homes from an existing database, pre-processed for the specific application. State-of-the-art GAN models are initially tested to establish baseline performance and applicability potential. Then, a systematic study is performed to identify the structure and hyperparameters necessary to successfully fit a GAN to this design task. The successful architecture, named 3DBuildingGAN, uses a combination of Wasserstein loss with gradient penalty, leaky rectified linear units for neuron activation in the generator and the critic, and the root mean squared propagation optimizer with a fixed learning rate. The proposed model generates outputs similar in size, shape, and proportion to the training data with minimal noise in the output. Evaluation of memorization properties indicates open research directions, such as incorporating memorization rejection and training on larger data sets. Finally, the study reflects on how AI algorithms can reshape creativity through data-driven design solutions.
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Generative Artificial Intelligence (AI) promises to make a vast impact across disciplines, including transforming the architectural design process by autonomously generating full building geometries. One form of generative deep learning that has been used to create 2D and 3D representations of objects is Generative Adversarial Networks (GANs). Existing literature, however, has limited applications that utilize 3D data for building geometry generation, with previous studies focused on low-scale 3D geometries suitable for objects such as chairs or cars. This paper develops a new GAN architecture to produce high-resolution feasible building geometry. The training dataset used is a selection of 3D models of single-family homes from an existing database, pre-processed for the specific application. State-of-the-art GAN models are initially tested to establish baseline performance and applicability potential. Then, a systematic study is performed to identify the structure and hyperparameters necessary to successfully fit a GAN to this design task. The successful architecture, named 3DBuildingGAN, uses a combination of Wasserstein loss with gradient penalty, leaky rectified linear units for neuron activation in the generator and the critic, and the root mean squared propagation optimizer with a fixed learning rate. The proposed model generates outputs similar in size, shape, and proportion to the training data with minimal noise in the output. Evaluation of memorization properties indicates open research directions, such as incorporating memorization rejection and training on larger data sets. Finally, the study reflects on how AI algorithms can reshape creativity through data-driven design solutions.
It is now within reach to use generative artificial intelligence (AI) to autonomously generate full building geometries. However, existing literature utilizing 3D data has focused to a limited degree on architecture and engineering disciplines. A critical first step to expanding the use of generative deep learning models in generative design research is making training data available. This study investigates 3D building model data characteristics that make it suitable for generative AI applications. Key data set attributes are identified through a systematic review of the object-containing datasets currently used to train state-of-the-art 3D GANs. These requirements are then compared to attributes of existing available building datasets. This comparison shows that publicly available data sets of 3D building models lack essential characteristics for generative deep learning. Features that make these building models inadequate for the task include but are not limited to, their mesh formats, low resolution and levels of detail, and inclusion of irrelevant geometry. To achieve the desired properties in this work, necessary transformations of the data are incorporated into a tailored preprocessing pipeline. The pipeline is applied to an existing dataset that contains 3D models of single-family homes. The transformed dataset is tested within state-of-the-art GAN models to assess training performance and document future data requirements for applying deep generative design to buildings. Our experiments show promise for the impact that architectural datasets can make on deep learning applications within the discipline. It also highlights the need for additional 3D building model data to increase the diversity and robustness of new designs.
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It is now within reach to use generative artificial intelligence (AI) to autonomously generate full building geometries. However, existing literature utilizing 3D data has focused to a limited degree on architecture and engineering disciplines. A critical first step to expanding the use of generative deep learning models in generative design research is making training data available. This study investigates 3D building model data characteristics that make it suitable for generative AI applications. Key data set attributes are identified through a systematic review of the object-containing datasets currently used to train state-of-the-art 3D GANs. These requirements are then compared to attributes of existing available building datasets. This comparison shows that publicly available data sets of 3D building models lack essential characteristics for generative deep learning. Features that make these building models inadequate for the task include but are not limited to, their mesh formats, low resolution and levels of detail, and inclusion of irrelevant geometry. To achieve the desired properties in this work, necessary transformations of the data are incorporated into a tailored preprocessing pipeline. The pipeline is applied to an existing dataset that contains 3D models of single-family homes. The transformed dataset is tested within state-of-the-art GAN models to assess training performance and document future data requirements for applying deep generative design to buildings. Our experiments show promise for the impact that architectural datasets can make on deep learning applications within the discipline. It also highlights the need for additional 3D building model data to increase the diversity and robustness of new designs.
Effective transportation network management systems should consider safety and sustainability objectives. Existing research on large-scale transportation network management often employs the assumption that bridges can be considered individually under these objectives. However, this simplification misses accurate system-level representations, induced by multiple components, network topology, and global maintenance actions. To address these limitations, this paper presents a deep reinforcement learning (DRL) framework that draws inspiration from biological learning behaviors to determine optimal life-cycle management policies. It incorporates synergetic branches and hierarchical rewards, factorizing the action space and, thereby, diminishing system complexity from exponential to linear with respect to the number of bridges. Extensive experiments based on a realistic case study demonstrate that the proposed method outperforms expert maintenance strategies and state-of-the-art decision-making methods. Overall, the proposed DRL framework can assist engineers by offering adaptive solutions to maintenance planning. It also provides solutions that address large action spaces within complex systems.
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Effective transportation network management systems should consider safety and sustainability objectives. Existing research on large-scale transportation network management often employs the assumption that bridges can be considered individually under these objectives. However, this simplification misses accurate system-level representations, induced by multiple components, network topology, and global maintenance actions. To address these limitations, this paper presents a deep reinforcement learning (DRL) framework that draws inspiration from biological learning behaviors to determine optimal life-cycle management policies. It incorporates synergetic branches and hierarchical rewards, factorizing the action space and, thereby, diminishing system complexity from exponential to linear with respect to the number of bridges. Extensive experiments based on a realistic case study demonstrate that the proposed method outperforms expert maintenance strategies and state-of-the-art decision-making methods. Overall, the proposed DRL framework can assist engineers by offering adaptive solutions to maintenance planning. It also provides solutions that address large action spaces within complex systems.
This work develops a computational method that produces algorithmically generated design forms, able to overcome inherent challenges related to the use of cast glass for the creation of monolithic structural components with light permeability. Structural Topology Optimization (TO) has a novel applicability potential, as decreased mass is associated with shorter annealing times and, thus, considerably improved manufacturability in terms of time, energy, and cost efficiency. However, realistic TO in such structures is currently hindered by existing mathematical formulations and commercial software capabilities. Incorporating annealing constraints into the optimization problem is an essential feature that needs to be accommodated, whereas the brittle nature of glass invokes asymmetric stress failure criteria that cannot be captured by conventional ductile plasticity surfaces or uniform stress constraints. This paper addresses the approximation problems in the evaluation of principal stresses while concurrently incorporating annealing-related manufacturing constraints into a unified TO formulation. A mass minimization objective is articulated, as this is the most critical factor for cast glass structures. To ensure the structural integrity and manufacturability of the component, the applied constraints refer both to the glass material/structural properties and to criteria that ensue from the annealing and fabrication processes. The developed code is based on the penalized artificial density interpolation scheme and the optimization problem is solved with the interior-point method. The proposed formulation is applied in a planar design domain to explore how different glass compositions and structural design strategies affect the final shape. Upon extraction of the optimized shape, the structural performance of the respective 3D structures is validated with respect to performance constraint violations using the Ansys software. Finally, brief guidelines on the practical aspects of the manufacturing process are provided.
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This work develops a computational method that produces algorithmically generated design forms, able to overcome inherent challenges related to the use of cast glass for the creation of monolithic structural components with light permeability. Structural Topology Optimization (TO) has a novel applicability potential, as decreased mass is associated with shorter annealing times and, thus, considerably improved manufacturability in terms of time, energy, and cost efficiency. However, realistic TO in such structures is currently hindered by existing mathematical formulations and commercial software capabilities. Incorporating annealing constraints into the optimization problem is an essential feature that needs to be accommodated, whereas the brittle nature of glass invokes asymmetric stress failure criteria that cannot be captured by conventional ductile plasticity surfaces or uniform stress constraints. This paper addresses the approximation problems in the evaluation of principal stresses while concurrently incorporating annealing-related manufacturing constraints into a unified TO formulation. A mass minimization objective is articulated, as this is the most critical factor for cast glass structures. To ensure the structural integrity and manufacturability of the component, the applied constraints refer both to the glass material/structural properties and to criteria that ensue from the annealing and fabrication processes. The developed code is based on the penalized artificial density interpolation scheme and the optimization problem is solved with the interior-point method. The proposed formulation is applied in a planar design domain to explore how different glass compositions and structural design strategies affect the final shape. Upon extraction of the optimized shape, the structural performance of the respective 3D structures is validated with respect to performance constraint violations using the Ansys software. Finally, brief guidelines on the practical aspects of the manufacturing process are provided.
In the context of modern engineering, environmental, and societal concerns, there is an increasing demand for methods able to identify rational management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level, often assuming statistical, structural, or cost independence among components, due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks, decoupling the originally joint system state space to component networks conditional on shared random variables. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.
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In the context of modern engineering, environmental, and societal concerns, there is an increasing demand for methods able to identify rational management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level, often assuming statistical, structural, or cost independence among components, due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks, decoupling the originally joint system state space to component networks conditional on shared random variables. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.
Journal article
(2023)
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A. Luna-Navarro, P. Khandhachani, E. Brembilla, P. de la Barra Luegmayer, C. Andriotis
User experience and satisfaction with the facade play a significant role in user comfort and energy efficiency of buildings. This paper explores the concept of User-Facade archetypes to inform the user-centred design of shading devices based on the perceived level of importance of different environmental domains at the workplace. A questionnaire was developed to collect data on users’ perceived level of importance of different environmental domains, user characteristics and other preferences. Based on the associated level of importance of the domains affected by shading devices (thermal conditions, access to daylight, access to outdoor view, privacy and glare mitigation), users were then clustered into eight different archetypes, which associated different "weights" to each comfort domain. The study also found a significant correlation between the associated level of importance and the reported frequency of interaction with shadings because of thermal comfort, glare mitigation or privacy. Overall, users that associated high levels of importance to several environmental domains also reported high perceived levels of importance for personal control at the workplace. Only one archetype reported low importance for personal control at the workplace. Further work is required to validate these archetypes by capturing actual user behaviour and preferences in real workplaces. However, these findings provide preliminary and valuable insights into the possibility of clustering users on their preferences and using this for informing a more user-centred design or operation of shading devices.
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User experience and satisfaction with the facade play a significant role in user comfort and energy efficiency of buildings. This paper explores the concept of User-Facade archetypes to inform the user-centred design of shading devices based on the perceived level of importance of different environmental domains at the workplace. A questionnaire was developed to collect data on users’ perceived level of importance of different environmental domains, user characteristics and other preferences. Based on the associated level of importance of the domains affected by shading devices (thermal conditions, access to daylight, access to outdoor view, privacy and glare mitigation), users were then clustered into eight different archetypes, which associated different "weights" to each comfort domain. The study also found a significant correlation between the associated level of importance and the reported frequency of interaction with shadings because of thermal comfort, glare mitigation or privacy. Overall, users that associated high levels of importance to several environmental domains also reported high perceived levels of importance for personal control at the workplace. Only one archetype reported low importance for personal control at the workplace. Further work is required to validate these archetypes by capturing actual user behaviour and preferences in real workplaces. However, these findings provide preliminary and valuable insights into the possibility of clustering users on their preferences and using this for informing a more user-centred design or operation of shading devices.
A key computational challenge in maintenance planning for deteriorating structures is to concurrently secure (i) optimality of decisions over long planning horizons, and (ii) accuracy of realtime parameter updates in high-dimensional stochastic spaces. Both are often encumbered by the presence of discretized continuous-state models that describe the underlying deterioration processes, and the emergence of combinatorial decision spaces due to multi-component environments. Recent advances in Deep Reinforcement Learning (DRL) formulations for inspection and maintenance planning provide us with powerful frameworks to handle efficiently near-optimal decision-making in immense state and action spaces without the need for offline system knowledge. Moreover, Bayesian Model Updating (BMU), aided by advanced sampling methods, allows us to address dimensionality and accuracy issues related to discretized degradation processes. Building upon these concepts, we develop a joint framework in this work, coupling DRL, more specifically deep Q-learning and actor-critic algorithms, with BMU through Hamiltonian Monte Carlo. Single- and multi-component systems are examined, and it is shown that the proposed methodology yields reduced lifelong maintenance costs, and policies of high fidelity and sophistication compared to traditional optimized time- and condition-based maintenance strategies.
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A key computational challenge in maintenance planning for deteriorating structures is to concurrently secure (i) optimality of decisions over long planning horizons, and (ii) accuracy of realtime parameter updates in high-dimensional stochastic spaces. Both are often encumbered by the presence of discretized continuous-state models that describe the underlying deterioration processes, and the emergence of combinatorial decision spaces due to multi-component environments. Recent advances in Deep Reinforcement Learning (DRL) formulations for inspection and maintenance planning provide us with powerful frameworks to handle efficiently near-optimal decision-making in immense state and action spaces without the need for offline system knowledge. Moreover, Bayesian Model Updating (BMU), aided by advanced sampling methods, allows us to address dimensionality and accuracy issues related to discretized degradation processes. Building upon these concepts, we develop a joint framework in this work, coupling DRL, more specifically deep Q-learning and actor-critic algorithms, with BMU through Hamiltonian Monte Carlo. Single- and multi-component systems are examined, and it is shown that the proposed methodology yields reduced lifelong maintenance costs, and policies of high fidelity and sophistication compared to traditional optimized time- and condition-based maintenance strategies.