The Scholarship of Teaching and Learning (SoTL) pertains to scholarly endeavors centered on the pedagogical aspects of teaching and learning, and its principal objective is the enhancement of students’ educational experiences. This systematic literature review addresses the following questions: (1) In what capacity do educators within the field of civil and structural engineering (CaSE) engage with SoTL?, and (2) What are the benefits of implementing a SoTL for CaSE educators? The scope of the review encompasses SoTL studies specifically developed by CaSE educators and implemented within CaSE teaching and learning environments. Findings are synthesized and disseminated via a bibliometric analysis and a narrative synthesis. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. It was found that CaSE educators participate in SoTL endeavors through diverse approaches; however, such involvement remains more of an exception than a common practice. The insufficiency of existing benefits and incentives, if any, serves as a barrier hindering broader engagement and participation in SoTL activities.

Industry 4.0 has led to digitalization and an increase in industrial activity. However, it has recently been recognized as inadequate for achieving European goals by 2030. Therefore, a novel Industry 5.0 paradigm has emerged in response to the unexpected negative effects caused by its predecessor. Industry 5.0 is mainly based on three foundational ideas: i) human-centrism, ii) resilience, and iii) sustainability. Human-centric solutions and human-machine-interaction; bio-inspired technologies and smart materials; real time-based digital twins and simulation; cyber safe data transmission, storage, and analysis; artificial intelligence; and energy efficiency and trustworthy autonomy have been recognized as the enabling technologies of this transformative vision. This paper outlines the protocol adopted to conduct a systematic literature review with the aim of exploring how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C) industry can adapt and be better prepared to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment.

The rise of Industry 4.0 has led to a rapid increase in digitalization and industrial operations. However, it has recently been deemed insufficient in fulfilling European objectives for 2030. In response, and to counteract the unintended negative consequences triggered by Industry 4.0, Industry 5.0 has been introduced. The purpose of this article is to shed light on how the architecture, engineering, construction, management, operation, and conservation industry can adapt and better prepare to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment. To achieve this, a systematic literature review was conducted following the PRISMA methodology. The principal results of this article highlight the work of different conservation professionals and our views on the potential of Industry 5.0 for enhancing conservation practices. Major conclusions indicate that artificial intelligence and digital twins are the two most studied technologies in the field. Sustainability is broadly discussed throughout the analyzed literature, whereas resilience and human centrism require further research and implementation efforts to achieve a holistic Industry 5.0 adoption. The significant scientific novelty of this work lies in the comprehensive scope of the review in terms of principles and enabling technologies, with a particular emphasis on heritage buildings. Thus, it is valuable for conservation practitioners seeking best practices, for policymakers as it suggests ways to encourage the adoption of novel technologies and principles in conservation, and for researchers as it highlights gaps and stimulates further paths of research and innovation.

Digital twins are expected to facilitate the digital transformation of the architecture, construction, engineering, management, operation, and conservation industry. One of their main applications in the field of bridge engineering is the possibility of real-time damage detection. To achieve this, advanced anomaly detection algorithms need to be validated to alert bridge operators on the risks related to the detected anomalies. A synthetic data generation framework has been proposed to generate benchmark databases for testing and validation as a cheaper and faster alternative to the collection of real monitoring data. In this paper we present the S101 Bridge, which has been selected as first case study for the generation of the proposed synthetic database. A finite element model of the bridge was developed and calibrated based on the reported natural frequencies and mode shapes of the actual structure available in the literature. The calibration process is described in detail along with the sensitivity analysis of the considered parameters. The results indicate a strong agreement between the first four natural frequencies and mode shapes of the experimental and numerical bridge model. It is concluded that the calibrated model is suitable for the generation of bride digital twins what-if scenarios, which will be of great significance for both practitioners and the research community.

The Digital Twin (DT) concept, as understood nowadays, appeared in the early 2000s as an attempt to create virtual replicas of physical assets, such as bridges, that can be used to examine, monitor and manage their performance. Up to this day, it has been successfully applied in the fields of aeronautics, manufacturing, medicine, and more recently, in the architecture, engineering, and construction industry. The DT of a bridge requires the creation of a virtual replica of the real-life asset, along with the connection and feedback of information channel between the two of them. This connection is currently achieved through the generation of real-time data by the placement of sensors in the real bridge and the application of structural health monitoring techniques to analyze such data. This connection could result in a complex, time-consuming, and expensive process which would hinder the creation of DT prototypes for development purposes in the bridge engineering field. This paper aims at exploring the currently available synthetic data generation methodologies and tools, which could be used as a faster and a more economically feasible alternative to real monitoring, for the creation and development of DT prototypes of bridges, for both industry and research-oriented purposes. A synthetic data generation framework is proposed that can produce FAIR benchmark databases that are based on Findability, Accessibility, Interoperability, and Reuse, which could be used in the prototyping of bridge DTs. Finally, tentative future improvements in this topic are discussed.

Earthen structures have an important representation among the UNESCO World Heritage List sites as well as among the built environment in general. Unfortunately, earthen heritage structures are also numerous within the UNESCO List of World Heritage in Danger whereas other existing common earthen structures are extremely vulnerable to seismic and climate change events. Within the field of heritage conservation, structural analysis contributes to the safety evaluation of the structure, the diagnosis of the causes of damage and decay, and to the validation of interventions. Thus, the need to develop effective and accurate computational methods suitable for the study of both monumental and vernacular earthen structures becomes evident. This paper compiles, summarizes, and highlights the latest developments and implementations of computational methods for the study of such structure typologies. The literature has been explored following the PRISMA-S checklist methodology and a narrative synthesis was used for the presentation of results. Finally, future trends, opportunities, and challenges are discussed.

Bridge infrastructure has great economic, social, and cultural value. Nevertheless, many of the infrastructural assets are in poor conservation condition as has been recently evidenced by the collapse of several bridges worldwide. The objective of this systematic review is to collect and synthesize state-of-the-art knowledge and information about how bridge information modeling, finite element modeling, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in bridges with cultural heritage value. A total of 76 relevant studies from 2017 up to 2022 have been taken into account in this review. The synthesis results show a consensus toward the future adoption of DT for bridge design, management, and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms, and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks.

Digital twins (DTs) are virtual replicas of physical assets that can be used to monitor and manage their performance. To date, the DT concept has been effectively implemented in various industries, including aeronautics, manufacturing, medicine, and more recently, in the architecture, engineering, and construction sector. In the latter, these assets can be related to buildings, bridges, or other important infrastructures of the built environment. Although the creation of synthetic benchmark datasets for the validation of novel damage detection approaches has been attempted in the past, such alternatives are not easily findable or accessible. Thus, a new synthetic data generation framework is proposed within the DT paradigm context, that can produce FAIR benchmark databases that are characterized by Findability, Accessibility, Interoperability, and Reuse. This paper aims at exploring the uncertainty types, sources, and quantification approaches involved in the synthetic data generation methodologies and tools of the intended framework which could be used as a faster and cheaper alternative to real monitoring, for the creation and development of DT prototypes of bridges for both industry and research-oriented purposes. This work also highlights the benefits and drawbacks of implementing synthetic data for these purposes and points out tentative future improvements in the field.