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 …
Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices
Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are emerging
techniques capable of delivering elegant and affordable solutions which can surpass those …
techniques capable of delivering elegant and affordable solutions which can surpass those …
Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls
RC shear walls are commonly used as lateral load-resisting elements in seismic regions,
and the estimation of their shear strengths can become simultaneously design-critical and …
and the estimation of their shear strengths can become simultaneously design-critical and …
Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners
The cementitious matrix of high-performance concrete (HPC) is highly complex, and
ambiguity exists with its mix design. Compressive strength can vary with the composition …
ambiguity exists with its mix design. Compressive strength can vary with the composition …
[HTML][HTML] Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China
W Zhang, H Li, L Han, L Chen, L Wang - Journal of Rock Mechanics and …, 2022 - Elsevier
Slope stability prediction plays a significant role in landslide disaster prevention and
mitigation. This study develops an ensemble learning-based method to predict the slope …
mitigation. This study develops an ensemble learning-based method to predict the slope …
Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements
This paper presents a practical yet comprehensive implementation of the ensemble methods
for prediction of the shear strength for reinforced concrete deep beams with/without web …
for prediction of the shear strength for reinforced concrete deep beams with/without web …
Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm
Machine learning techniques are widely used algorithms for predicting the mechanical
properties of concrete. This study is based on the comparison of algorithms between …
properties of concrete. This study is based on the comparison of algorithms between …
A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis
R Wang, H Jiang, K Zhu, Y Wang, C Liu - Advanced Engineering …, 2022 - Elsevier
Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in
practical engineering, the fault modes of rolling bearings are usually compound faults and …
practical engineering, the fault modes of rolling bearings are usually compound faults and …
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 …
Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential
Among the research hotspots in geological/geotechnical engineering, research on the
prediction of soil liquefaction potential is still limited. In this research, several machine …
prediction of soil liquefaction potential is still limited. In this research, several machine …