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 …
Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review
The mixture proportioning of conventional concrete is commonly established using
regression analysis of experimental data. However, such traditional empirical procedures …
regression analysis of experimental data. However, such traditional empirical procedures …
[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 …
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
This study aims to implement a hybrid ensemble surrogate machine learning technique in
predicting the compressive strength (CS) of concrete, an important parameter used for …
predicting the compressive strength (CS) of concrete, an important parameter used for …
A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement
Compressive strength of concrete is one of the most determinant parameters in the design of
engineering structures. This parameter is generally determined by conducting several tests …
engineering structures. This parameter is generally determined by conducting several tests …
Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
Abstract Machine learning (ML)-based prediction of non-linear composition-strength
relationship in concretes requires a large, complete, and consistent dataset. However, the …
relationship in concretes requires a large, complete, and consistent dataset. However, the …
A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI)
techniques, namely Gaussian Process Regression (GPR) with five different kernels …
techniques, namely Gaussian Process Regression (GPR) with five different kernels …
A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis …
Abstract In this study, Multiple Regression Analysis (MRA) and Artificial Neural Network
(ANN) models are constructed to predict the compressive strength of High Performance …
(ANN) models are constructed to predict the compressive strength of High Performance …
Soft computing techniques in structural and earthquake engineering: a literature review
R Falcone, C Lima, E Martinelli - Engineering Structures, 2020 - Elsevier
Although civil engineering problems are often characterized by significant levels of
complexity, they are generally approached and solved by combining several practitioners' …
complexity, they are generally approached and solved by combining several practitioners' …
Soft computing techniques: systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times
Advances in technology and environmental issues allow the building industry to use ever
more high-performance engineered materials. In this study, the hardness of concrete …
more high-performance engineered materials. In this study, the hardness of concrete …