Machine learning in concrete science: applications, challenges, and best practices
Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …
human development. Despite conceptual and methodological progress in concrete science …
Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
S Wang, P Xia, K Chen, F Gong, H Wang… - Journal of Building …, 2023 - Elsevier
Among the many sustainability challenges in the construction industry, those related to the
application of concrete and its components are the most critical. Particularly, the production …
application of concrete and its components are the most critical. Particularly, the production …
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 …
Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks
Cracks are common defects in slab tracks, which can grow and expand over time, leading to
a deterioration of the mechanical properties of slab tracks and shortening service life …
a deterioration of the mechanical properties of slab tracks and shortening service life …
Deep neural network with high‐order neuron for the prediction of foamed concrete strength
The article presents a deep neural network model for the prediction of the compressive
strength of foamed concrete. A new, high‐order neuron was developed for the deep neural …
strength of foamed concrete. A new, high‐order neuron was developed for the deep neural …
Supervised deep restricted Boltzmann machine for estimation of concrete
Costly and time-consuming destructive methods are usually used to determine the
properties of alternative concrete mixtures. To reduce cost and time, statistical and neural …
properties of alternative concrete mixtures. To reduce cost and time, statistical and neural …
Mechanical–transport–chemical modeling of electrochemical repair methods for corrosion‐induced cracking in marine concrete
Reinforced concrete structures exposed to marine environments often experience chloride
ingress, reinforcement corrosion, and corrosion‐induced cracking. The electrochemical …
ingress, reinforcement corrosion, and corrosion‐induced cracking. The electrochemical …
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
One of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the
ability to anticipate their flexural response. With a comprehensive grid search, several …
ability to anticipate their flexural response. With a comprehensive grid search, several …
Modeling the chloride transport in concrete from microstructure generation to chloride diffusivity prediction
Pore structure characteristics of cementitious materials play a critical role in the transport
properties of concrete structures. This paper develops a novel framework for modeling …
properties of concrete structures. This paper develops a novel framework for modeling …
A graph‐based method for quantifying crack patterns on reinforced concrete shear walls
This paper presents an innovative method to quantify damage based on surface cracks of
reinforced concrete shear walls (RCSWs). The key idea is to use artificial intelligence and …
reinforced concrete shear walls (RCSWs). The key idea is to use artificial intelligence and …