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 …
Predictive models for concrete properties using machine learning and deep learning approaches: A review
Concrete is one of the most widely used materials in various civil engineering applications.
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of …
Fly ash is a by-product almost found in coal power plants; it is available worldwide.
According to the hazardous impacts of cement on the environment, fly ash is known to be a …
According to the hazardous impacts of cement on the environment, fly ash is known to be a …
[HTML][HTML] Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms
The cementitious composites have different properties in the changing environment. Thus,
knowing their mechanical properties is very important for safety reasons. The most important …
knowing their mechanical properties is very important for safety reasons. The most important …
[HTML][HTML] A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations …
Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …
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 …
Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete
Steel fiber-reinforced concrete (SFRC) has a performance superior to that of normal
concrete because of the addition of discontinuous fibers. The development of strengths …
concrete because of the addition of discontinuous fibers. The development of strengths …
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 …
Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation
X Shi, X Yu, M Esmaeili-Falak - Composite Structures, 2023 - Elsevier
In order to restore steel structures, bonding carbon fiber reinforced polymer (CFRP)
laminates have been widely used. The bond strength (PU) between the CFRP and steel …
laminates have been widely used. The bond strength (PU) between the CFRP and steel …
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 …