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 …
Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
Y Wu, Y Zhou - Construction and Building Materials, 2022 - Elsevier
The application of the traditional support vector regression (SVR) model to predict the
compressive strength of concrete faces the challenge of parameter tuning. To this end, a …
compressive strength of concrete faces the challenge of parameter tuning. To this end, a …
[HTML][HTML] Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
This paper presents a data-driven approach to determine the load and flexural capacities of
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …
[HTML][HTML] Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method
Accurate prediction of shield tunneling-induced settlement is a complex problem that
requires consideration of many influential parameters. Recent studies reveal that machine …
requires consideration of many influential parameters. Recent studies reveal that machine …
Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation
To provide more accurate and reliable predictions of the shear strength of ultrahigh-
performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches …
performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches …
Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a" conscious lab" approach
Cement production is one of the most energy-intensive manufacturing industries, and the
milling circuit of cement plants consumes around 4% of a year's global electrical energy …
milling circuit of cement plants consumes around 4% of a year's global electrical energy …
[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 …
Interpretable machine learning methods for clarification of load-displacement effects on cable-stayed bridge
Cable-stayed bridges play a crucial role in various transportation systems, facilitating the
movement of pedestrians, automobiles, and trains. Accurately estimating structural …
movement of pedestrians, automobiles, and trains. Accurately estimating structural …
Explainable machine learning model for predicting punching shear strength of FRC flat slabs
Reinforced concrete slabs are vulnerable to punching shear failure at the slab-column joint,
which can initiate catastrophic progressive collapse. The addition of steel fibers in the …
which can initiate catastrophic progressive collapse. The addition of steel fibers in the …