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 …
State-of-the-art AI-based computational analysis in civil engineering
C Wang, L Song, Z Yuan, J Fan - Journal of Industrial Information …, 2023 - Elsevier
With the informatization of the building and infrastructure industry, conventional analysis
methods are gradually proving inadequate in meeting the demands of the new era, such as …
methods are gradually proving inadequate in meeting the demands of the new era, such as …
Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
Abstract Predicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting
Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of …
Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of …
Novel hybrid MFO-XGBoost model for predicting the racking ratio of the rectangular tunnels subjected to seismic loading
This study proposes a novel hybrid MFO-XGBoost model that integrates the moth-flame
optimization (MFO) algorithm and the extreme gradient boosting (XGBoost) to predict the …
optimization (MFO) algorithm and the extreme gradient boosting (XGBoost) to predict the …
Rapid seismic damage-state assessment of steel moment frames using machine learning
The damage state assessment of buildings after an earthquake is an essential and urgent
task that typically requires significant manpower and time for the resilience of a city-scale …
task that typically requires significant manpower and time for the resilience of a city-scale …
Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms
W Zhu, L Huang, L Mao… - Structural Concrete, 2022 - Wiley Online Library
Because natural coarse aggregates were depleting rapidly, concrete industry has been
trended toward substitute aggregates from industrial by‐products or waste. One of the waste …
trended toward substitute aggregates from industrial by‐products or waste. One of the waste …
[HTML][HTML] Enhancing earthquakes and quarry blasts discrimination using machine learning based on three seismic parameters
Explosions and other artificial seismic sources remain a major risk to human survival.
Seismicity catalogs often suffer from contamination, which hinders the differentiation of …
Seismicity catalogs often suffer from contamination, which hinders the differentiation of …
Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry …
This study presents a comprehensive and rigorous process to develop the most appropriate
machine learning (ML) model for predicting the shear strength of RC deep beams (RCDBs) …
machine learning (ML) model for predicting the shear strength of RC deep beams (RCDBs) …
Boosting machines for predicting shear strength of CFS channels with staggered web perforations
Cold-formed steel (CFS) purlins and studs with staggered web perforations have been used
in construction to improve the thermal efficiency of buildings. The perforations adversely …
in construction to improve the thermal efficiency of buildings. The perforations adversely …
Seismic fragility analysis of steel moment frames using machine learning models
This study develops machine learning (ML) models for seismic fragility analysis of steel
moment frames. Four ML methods–random forest, adaptive boosting, gradient boosting …
moment frames. Four ML methods–random forest, adaptive boosting, gradient boosting …