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 …
Shear strength prediction of reinforced concrete beams using machine learning
Recent years have witnessed a surge in the application of machine learning techniques for
solving hard to solve structural engineering problems. The application of machine learning …
solving hard to solve structural engineering problems. The application of machine learning …
Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete
Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength.
However, the understanding of ISF's influence on the compressive strength (CS) behavior of …
However, the understanding of ISF's influence on the compressive strength (CS) behavior of …
LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis
Concrete production contributes significantly to global greenhouse gas emissions, and its
manufacture requires substantial natural resources. These concerns can be partly mitigated …
manufacture requires substantial natural resources. These concerns can be partly mitigated …
Development of novel design strength model for sustainable concrete columns: A new machine learning-based approach
Billions of tons of construction and demolition (C&D) waste generation is causing global
environmental crises. The application of C&D waste in concrete columns is a sustainable …
environmental crises. The application of C&D waste in concrete columns is a sustainable …
A comparison of machine learning tools that model the splitting tensile strength of self-compacting recycled aggregate concrete
J de-Prado-Gil, C Palencia, P Jagadesh… - Materials, 2022 - mdpi.com
Several types of research currently use machine learning (ML) methods to estimate the
mechanical characteristics of concrete. This study aimed to compare the capacities of four …
mechanical characteristics of concrete. This study aimed to compare the capacities of four …
Supplementary cementitious materials in blended cement concrete: Advancements in predicting compressive strength through machine learning
The increasing utilization of Portland cement raises environmental concerns. Thus, leading
to the exploration of supplementary cementitious materials (SCMs) as alternatives to use in …
to the exploration of supplementary cementitious materials (SCMs) as alternatives to use in …
Benchmarking AutoML for regression tasks on small tabular data in materials design
F Conrad, M Mälzer, M Schwarzenberger, H Wiemer… - Scientific Reports, 2022 - nature.com
Abstract Machine Learning has become more important for materials engineering in the last
decade. Globally, automated machine learning (AutoML) is growing in popularity with the …
decade. Globally, automated machine learning (AutoML) is growing in popularity with the …
Optimization of rice husk ash concrete design towards economic and environmental assessment
Rice husk ash (RHA) is one of the main agricultural wastes that holds great potential for
utilization in concrete production. Previous studies have mainly focused on its technical …
utilization in concrete production. Previous studies have mainly focused on its technical …
Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and …
The extensive use of plastics has resulted in significant environmental challenges due to
non-biodegradable waste. Researchers explore the integration of plastic waste into …
non-biodegradable waste. Researchers explore the integration of plastic waste into …