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 …
Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …
to increased exposure and vulnerability of structural and infrastructure systems to hazards …
Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete
Reinforced concrete bond strength deterioration is one of the most serious problems in the
construction industry. It is one of the most common factors impacting structural deterioration …
construction industry. It is one of the most common factors impacting structural deterioration …
[HTML][HTML] Testing and modeling methods to experiment the flexural performance of cement mortar modified with eggshell powder
Sustainable development might be promoted if waste eggshells are used in cement-based
materials (CBMs) by decreasing waste disposal problems, CO 2 emissions, and material …
materials (CBMs) by decreasing waste disposal problems, CO 2 emissions, and material …
[HTML][HTML] Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites
The application of inorganic composites has proven to be an effective strengthening
technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of …
technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of …
Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach
Corrosion of steel reinforcements is a major factor that will adversely affect the seismic
performance of the reinforced concrete (RC) columns. This paper investigates the …
performance of the reinforced concrete (RC) columns. This paper investigates the …
[HTML][HTML] Toward explainable electrical load forecasting of buildings: A comparative study of tree-based ensemble methods with Shapley values
Electrical load forecasting of buildings is crucial in designing an energy operation strategy
for smart city realization. Although artificial intelligence techniques have demonstrated …
for smart city realization. Although artificial intelligence techniques have demonstrated …
Beyond the chloride threshold concept for predicting corrosion of steel in concrete
All existing models to forecast the corrosion performance of reinforced concrete structures
exposed to chloride environments are based on one common theoretical concept, namely, a …
exposed to chloride environments are based on one common theoretical concept, namely, a …
[HTML][HTML] FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model
Fiber-reinforced polymer (FRP) composites have recently been considered in the field of
structural engineering as one of the best alternatives to conventional steel reinforcement …
structural engineering as one of the best alternatives to conventional steel reinforcement …
An enhanced PDEM-based framework for reliability analysis of structures considering multiple failure modes and limit states
In this paper, an enhanced probability density evolution method (PDEM) framework
considering multiple failure modes and limit states is proposed for reliability analysis of …
considering multiple failure modes and limit states is proposed for reliability analysis of …