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 …
[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 …
Efficient machine learning models for prediction of concrete strengths
In this study, an efficient implementation of machine learning models to predict compressive
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …
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 …
State-of-the-art review on advancements of data mining in structural health monitoring
To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …
statistical methods have been utilized in a remarkable number of structural health monitoring …
A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC)
Supervised machine learning and its algorithm is an emerging trend for the prediction of
mechanical properties of concrete. This study uses an ensemble random forest (RF) and …
mechanical properties of concrete. This study uses an ensemble random forest (RF) and …
Compressive Strength of Fly‐Ash‐Based Geopolymer Concrete by Gene Expression Programming and Random Forest
Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the
production of FA‐based geopolymer concrete (FGPC). To avoid time‐consuming and costly …
production of FA‐based geopolymer concrete (FGPC). To avoid time‐consuming and costly …
A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm
Q Han, C Gui, J Xu, G Lacidogna - Construction and Building Materials, 2019 - Elsevier
The prediction results of high-performance concrete compressive strength (HPCCS) based
on machine learning methods are seriously influenced by input variables and model …
on machine learning methods are seriously influenced by input variables and model …
A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models
Z Wang, RS Srinivasan - Renewable and Sustainable Energy Reviews, 2017 - Elsevier
Building energy use prediction plays an important role in building energy management and
conservation as it can help us to evaluate building energy efficiency, conduct building …
conservation as it can help us to evaluate building energy efficiency, conduct building …