Machine learning for structural engineering: A state-of-the-art review
HT Thai - Structures, 2022 - Elsevier
Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …
intelligence (AI). It provides a unique opportunity to make structural engineering more …
Evaluating the fire risk associated with cladding panels: An overview of fire incidents, policies, and future perspective in fire standards
Multifunctional building façades have become an increasingly critical component in modern
buildings, especially after the tremendous scrutiny triggered by the utilization of combustible …
buildings, especially after the tremendous scrutiny triggered by the utilization of combustible …
The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network
Search for enhancing the efficiency has led to composite structures such as concrete-filled
steel tubes (CFST) with increasing applications across the world. The fire performance of …
steel tubes (CFST) with increasing applications across the world. The fire performance of …
Fire induced progressive collapse potential assessment of steel framed buildings using machine learning
F Fu - Journal of Constructional Steel Research, 2020 - Elsevier
In this paper, a new Machine Learning framework is developed for fast prediction of the
failure patterns of simple steel framed buildings in fire and subsequent progressive collapse …
failure patterns of simple steel framed buildings in fire and subsequent progressive collapse …
Deriving temperature-dependent material models for structural steel through artificial intelligence
MZ Naser - Construction and Building Materials, 2018 - Elsevier
Structural steel undergoes significant metallurgical and physio-chemical degradation under
fire conditions. This degradation is often represented by temperature-dependent material …
fire conditions. This degradation is often represented by temperature-dependent material …
Prediction of fire resistance of concrete encased steel composite columns using artificial neural network
Concrete encased steel (CES) columns, also known as steel reinforced concrete (SRC)
composite columns, exhibit superior fire resistance due to concrete acting as a protection …
composite columns, exhibit superior fire resistance due to concrete acting as a protection …
Neural networks for predicting shear strength of CFS channels with slotted webs
VV Degtyarev - Journal of Constructional Steel Research, 2021 - Elsevier
Cold-formed steel channels are made with staggered courses of slots for reduced thermal
conductivity and improved energy efficiency of cold-formed steel buildings. The reduced …
conductivity and improved energy efficiency of cold-formed steel buildings. The reduced …
Prediction of critical buckling load of web tapered I-section steel columns using artificial neural networks
The web tapered I-section steel (WTIS) columns have been widely used in civil and
industrial steel structures. However, the existing theoretical and empirical equations …
industrial steel structures. However, the existing theoretical and empirical equations …
[HTML][HTML] Numerical evaluation of the effects of fire on steel connections; Part 1: Simulation techniques
R Rahnavard, RJ Thomas - Case Studies in Thermal Engineering, 2018 - Elsevier
Steel connections are used to connect between beam and column in steel moment frame
structures. As of present time, there is a huge lack of understanding of the performance of …
structures. As of present time, there is a huge lack of understanding of the performance of …
Predicting heat release properties of flammable fiber-polymer laminates using artificial neural networks
Heat release rate is an important fire reaction property used to quantify the flammability of
composite materials in fire. In this study, an artificial neural network (ANN) model was …
composite materials in fire. In this study, an artificial neural network (ANN) model was …