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 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 …
Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications
W Fan, Y Chen, J Li, Y Sun, J Feng, H Hassanin… - Structures, 2021 - Elsevier
Abstract Machine learning is one of the key pillars of industry 4.0 that has enabled rapid
technological advancement through establishing complex connections among …
technological advancement through establishing complex connections among …
Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete
EM Golafshani, A Behnood - Journal of cleaner production, 2018 - Elsevier
The use of recycled concrete aggregate (RCA) as a replacement for natural aggregate in
concrete mixtures provides a wide variety of benefits such as reduced cost and pollution …
concrete mixtures provides a wide variety of benefits such as reduced cost and pollution …
Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach
This article presents the feasibility of using support vector regression (SVR) technique to
determine the fresh and hardened properties of self-compacting concrete. Two different …
determine the fresh and hardened properties of self-compacting concrete. Two different …
Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete
With its growing emphasis on sustainability, the construction industry is increasingly
interested in environmentally friendly concrete produced by using alternative and/or …
interested in environmentally friendly concrete produced by using alternative and/or …
Predicting the mechanical properties of cement mortar using the support vector machine approach
S Jueyendah, M Lezgy-Nazargah… - … and Building Materials, 2021 - Elsevier
In this paper, in order to predict the flexural strength and compressive strength of cement
mortar containing nano-silica (NS) and micro-silica (MS), the possibility of using the support …
mortar containing nano-silica (NS) and micro-silica (MS), the possibility of using the support …
Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete
EM Golafshani, A Behnood - Applied Soft Computing, 2018 - Elsevier
The use of recycled concrete aggregate to produce new concrete can assist the
sustainability in construction industry. However, the mechanical properties of this type of …
sustainability in construction industry. However, the mechanical properties of this type of …
Modelling the fresh properties of self-compacting concrete using support vector machine approach
The main objective of the study presented in this paper was to investigate the feasibility
using support vector machines (SVM) for the prediction of the fresh properties of self …
using support vector machines (SVM) for the prediction of the fresh properties of self …
A novel hybrid radial basis function method for predicting the fresh and hardened properties of self-compacting concrete
Z Nurlan - Advances in Engineering and Intelligence Systems, 2022 - aeis.bilijipub.com
It is observed from the published literature that there were so limited studies concentrating
on predicting both fresh or hardened properties of self-compacting concrete (SCC). Hence, it …
on predicting both fresh or hardened properties of self-compacting concrete (SCC). Hence, it …