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Mhd Anwar Orabi
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4 records found
1
Book chapter
(2024)
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Mhd Anwar Orabi, Zhuojun Nan, Asif Usmani
Designing and constructing a building is a complex process with many stakeholders, constraints, and requirements. Generally, a property development firm would hire an architect to produce a concept design that is meant to achieve the client objectives. After a few iterations, and if the development firm and client succeeded in obtaining initial funding for the project, they move on to hire design, construction, and possibly project management firms. The structural engineering designer is one of the various engineering teams involved in the project and is responsible for ensuring that the structure is designed with sufficient capacity to be safe under all expected loading conditions [1].
Typically, the structural engineer operates under the supervision of the architect and the project manager and liaises with the contractor and other engineering disciplines such as those dealing with fire, ventilation, plumbing, and electrical systems. Naturally, each of these engineering disciplines have their own objectives for their part of the project, and thus conflicts often arise and are resolved by interdisciplinary compromise and cooperation. Structural fire engineering is unique in its nature as it is, by definition, a cross-disciplinary enterprise that is concerned with both fire and structural behaviour. Unfortunately, current practice is that structural fire design is performed as a “check” rather than a part of a holistic design approach. This means that the structural fire engineer is often given the constraints of a mostly finished structural design that they need to ensure remains safe under any potential fire scenarios. The time given for such a critical undertaking is usually in the range of one to three weeks. This chapter will cover how automation of the most repetitive and time-consuming parts of structural fire engineering may enable the engineer to perform a thorough structural fire analysis within the tight limitations of realistic project timelines. ...
Typically, the structural engineer operates under the supervision of the architect and the project manager and liaises with the contractor and other engineering disciplines such as those dealing with fire, ventilation, plumbing, and electrical systems. Naturally, each of these engineering disciplines have their own objectives for their part of the project, and thus conflicts often arise and are resolved by interdisciplinary compromise and cooperation. Structural fire engineering is unique in its nature as it is, by definition, a cross-disciplinary enterprise that is concerned with both fire and structural behaviour. Unfortunately, current practice is that structural fire design is performed as a “check” rather than a part of a holistic design approach. This means that the structural fire engineer is often given the constraints of a mostly finished structural design that they need to ensure remains safe under any potential fire scenarios. The time given for such a critical undertaking is usually in the range of one to three weeks. This chapter will cover how automation of the most repetitive and time-consuming parts of structural fire engineering may enable the engineer to perform a thorough structural fire analysis within the tight limitations of realistic project timelines. ...
Designing and constructing a building is a complex process with many stakeholders, constraints, and requirements. Generally, a property development firm would hire an architect to produce a concept design that is meant to achieve the client objectives. After a few iterations, and if the development firm and client succeeded in obtaining initial funding for the project, they move on to hire design, construction, and possibly project management firms. The structural engineering designer is one of the various engineering teams involved in the project and is responsible for ensuring that the structure is designed with sufficient capacity to be safe under all expected loading conditions [1].
Typically, the structural engineer operates under the supervision of the architect and the project manager and liaises with the contractor and other engineering disciplines such as those dealing with fire, ventilation, plumbing, and electrical systems. Naturally, each of these engineering disciplines have their own objectives for their part of the project, and thus conflicts often arise and are resolved by interdisciplinary compromise and cooperation. Structural fire engineering is unique in its nature as it is, by definition, a cross-disciplinary enterprise that is concerned with both fire and structural behaviour. Unfortunately, current practice is that structural fire design is performed as a “check” rather than a part of a holistic design approach. This means that the structural fire engineer is often given the constraints of a mostly finished structural design that they need to ensure remains safe under any potential fire scenarios. The time given for such a critical undertaking is usually in the range of one to three weeks. This chapter will cover how automation of the most repetitive and time-consuming parts of structural fire engineering may enable the engineer to perform a thorough structural fire analysis within the tight limitations of realistic project timelines.
Typically, the structural engineer operates under the supervision of the architect and the project manager and liaises with the contractor and other engineering disciplines such as those dealing with fire, ventilation, plumbing, and electrical systems. Naturally, each of these engineering disciplines have their own objectives for their part of the project, and thus conflicts often arise and are resolved by interdisciplinary compromise and cooperation. Structural fire engineering is unique in its nature as it is, by definition, a cross-disciplinary enterprise that is concerned with both fire and structural behaviour. Unfortunately, current practice is that structural fire design is performed as a “check” rather than a part of a holistic design approach. This means that the structural fire engineer is often given the constraints of a mostly finished structural design that they need to ensure remains safe under any potential fire scenarios. The time given for such a critical undertaking is usually in the range of one to three weeks. This chapter will cover how automation of the most repetitive and time-consuming parts of structural fire engineering may enable the engineer to perform a thorough structural fire analysis within the tight limitations of realistic project timelines.
Journal article
(2023)
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Zhiruoyu Wang, Mhd Anwar Orabi, Zhuojun Nan, Weiyong Wang, Matthew Mason, David Lange
The diagrid structural system has seen significant uptake in medium to high rise buildings because of the architectural and resource advantages that it provides. These arise mainly as a result of flexibility in the topology that this particular structural solution provides. However, as a result of the way in which diagrids carry both horizontal and vertical loading, the diagrid structure itself may be susceptible to fire in ways which are not immediately obvious on the basis of our understanding of more traditional rectilinear construction forms. This study addresses this to improve our understanding of diagrid structures' response to fire. A comprehensive structural analysis on 45 fire load cases is conducted, considering different fire locations and sizes, using parametric design tools and finite element analysis software. The results provide valuable insights into the load redistribution and collapse mechanisms of diagrid structures in fire conditions.
...
The diagrid structural system has seen significant uptake in medium to high rise buildings because of the architectural and resource advantages that it provides. These arise mainly as a result of flexibility in the topology that this particular structural solution provides. However, as a result of the way in which diagrids carry both horizontal and vertical loading, the diagrid structure itself may be susceptible to fire in ways which are not immediately obvious on the basis of our understanding of more traditional rectilinear construction forms. This study addresses this to improve our understanding of diagrid structures' response to fire. A comprehensive structural analysis on 45 fire load cases is conducted, considering different fire locations and sizes, using parametric design tools and finite element analysis software. The results provide valuable insights into the load redistribution and collapse mechanisms of diagrid structures in fire conditions.
Journal article
(2023)
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Zhuojun Nan, Mhd Anwar Orabi, Xinyan Huang, Yaqiang Jiang, Asif Usmani
This study analyses the structural response of an aluminium reticulated roof structure that is constructed at Sichuan Fire Research Institute (Sichuan, China), and to be tested in fire. The structural fire behaviour under 960 localised fire scenarios is considered first, and then used to construct a database for training a modular artificial intelligence (AI) system for real-time forecasting. The system consists of several AI models, each of which predicts the displacement at a specific monitoring point. These individual predictions are then combined to generate a comprehensive forecast of the global structural-fire behaviour. The individual AI model utilized is a Long Short-Term Memory Recurrent Neural Network (LSTM RNN). The modular design allows different models to be modified or added as needed, making the system flexible and adaptable, and improving the accuracy and reliability of the predictions. The results demonstrate the effectiveness of the modular AI approach in accurately forecasting fire-induced structural collapses as indicated by the sensitivity the local models can have. The key objective of this research is to help to make informed decisions and prioritize efforts to minimize the risk of structural collapse in fire.
...
This study analyses the structural response of an aluminium reticulated roof structure that is constructed at Sichuan Fire Research Institute (Sichuan, China), and to be tested in fire. The structural fire behaviour under 960 localised fire scenarios is considered first, and then used to construct a database for training a modular artificial intelligence (AI) system for real-time forecasting. The system consists of several AI models, each of which predicts the displacement at a specific monitoring point. These individual predictions are then combined to generate a comprehensive forecast of the global structural-fire behaviour. The individual AI model utilized is a Long Short-Term Memory Recurrent Neural Network (LSTM RNN). The modular design allows different models to be modified or added as needed, making the system flexible and adaptable, and improving the accuracy and reliability of the predictions. The results demonstrate the effectiveness of the modular AI approach in accurately forecasting fire-induced structural collapses as indicated by the sensitivity the local models can have. The key objective of this research is to help to make informed decisions and prioritize efforts to minimize the risk of structural collapse in fire.
Conference paper
(2022)
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Zhuojun Nan, Mhd Anwar Orabi, Xiaoning Zhang, Aatif Ali Khan, Xinyan Huang, Liming Jiang, Yaqiang Jiang, Asif Usmani
First respondents to fires in structures face severe risks as both the fire and structural behaviour are unpredictable. While structural collapse may manifest some warning signs, these signs are not always easily identified which has led to the death of many fire fighters over the years. Both fire and structural fire simulation have come a long way and are now capable of assessing the thermomechanical behaviour of structures to a good degree of accuracy. However, such simulations take hundreds or thousands of engineering and computation hours. This paper explores performing these analyses a priori and using the generated database to train a recurrent neural network for real time prediction of potential failure. The analysis is performed on an aluminium reticulated roof structure that is constructed in Sichuan Fire Research Institute (Sichuan, China) and is expected to be tested to failure in fire in 2023. One hundred localised fire scenarios were used to cover the potential fire that will be used to induce the failure of the test roof. Heat transfer analyses for each section were then performed in OpenSEES followed by thermomechanical analysis in the same software. The generated results database was then cleaned and the data at several key locations were extracted and used to train a long short term memory recurrent neural network. The results of the predictions show that the artificial intelligence model can infer results with increasing accuracy the closer the structure is to failure. The real test of the accuracy of the model, however, will be during the fire experiment on the real structure. This would be the first time an artificial intelligence model for rapid forecasting of structural response in fire is built a priori and tested against a real fire.
...
First respondents to fires in structures face severe risks as both the fire and structural behaviour are unpredictable. While structural collapse may manifest some warning signs, these signs are not always easily identified which has led to the death of many fire fighters over the years. Both fire and structural fire simulation have come a long way and are now capable of assessing the thermomechanical behaviour of structures to a good degree of accuracy. However, such simulations take hundreds or thousands of engineering and computation hours. This paper explores performing these analyses a priori and using the generated database to train a recurrent neural network for real time prediction of potential failure. The analysis is performed on an aluminium reticulated roof structure that is constructed in Sichuan Fire Research Institute (Sichuan, China) and is expected to be tested to failure in fire in 2023. One hundred localised fire scenarios were used to cover the potential fire that will be used to induce the failure of the test roof. Heat transfer analyses for each section were then performed in OpenSEES followed by thermomechanical analysis in the same software. The generated results database was then cleaned and the data at several key locations were extracted and used to train a long short term memory recurrent neural network. The results of the predictions show that the artificial intelligence model can infer results with increasing accuracy the closer the structure is to failure. The real test of the accuracy of the model, however, will be during the fire experiment on the real structure. This would be the first time an artificial intelligence model for rapid forecasting of structural response in fire is built a priori and tested against a real fire.