Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 22, P. e04112 - e04112

Published: Dec. 11, 2024

Language: Английский

---

Utilizing contemporary machine learning techniques for determining soilcrete properties

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Abstract Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other are not readily available due to financial or environmental reasons since soilcrete from natural clay. also help cut down the greenhouse gas emissions industry encouraging use of resources that locally available. Thus, it imperative reliably predict different properties accurate determination these crucial for widespread materials. However, laboratory subjected significant time and resource constraints. As a result, this research was undertaken provide empirical prediction models density, shrinkage, strain mixes using two machine learning algorithms: Gene Expression Programming (GEP) Extreme Gradient Boosting (XGB). The analysis revealed XGB-based predictions correlated more real-life values than GEP having training $${\text{R}}^{2}=0.999$$ R 2 = 0.999 both density shrinkage $${\text{R}}^{2}=0.944$$ 0.944 prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) shapely were done on XGB model which showed water-to-binder ratio, metakaolin content, modulus elasticity some most important variables forecasting properties. Furthermore, interactive graphical user interface (GUI) has been developed effective utilization civil engineering forecast

Language: Английский

---

Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

Interface yield stress and plastic viscosity of fresh concrete significantly influences its pumping ability. The accurate determination these properties needs extensive testing on-site which results in time resource wastage. Thus, to speed up the process accurately determining properties, this study tends use four machine learning (ML) algorithms including Random Forest Regression (RFR), Gene Expression Programming (GEP), K-nearest Neighbor (KNN), Extreme Gradient Boosting (XGB) a statistical technique Multi Linear (MLR) develop predictive models for interface concrete. Out all employed algorithms, only GEP expressed output form an empirical equation. were developed using data from published literature having six input parameters cement, water, after mixing etc. two i.e., stress. performance was assessed several error metrices, k-fold validation, residual assessment comparison revealed that XGB is most algorithm predict (training [Formula: see text], text]) text]). To get increased insights into model prediction process, shapely individual conditional expectation analyses carried out on highlighted are influential estimate both In addition, graphical user has been made efficiently implement findings civil engineering industry.

Language: Английский

---

Reliable determination of peak shear strength of H-shaped concrete squat walls using explainable machine learning techniques

Structures, Journal Year: 2025, Volume and Issue: 76, P. 108802 - 108802

Published: April 14, 2025

Language: Английский

---

Hybrid Machine Learning Based Strength and Durability Predictions of Polypropylene Fiber-Reinforced Graphene Oxide Based High-Performance Concrete

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 19, 2025

Language: Английский

---

Empirical Formulas and Artificial Neural Networks to Estimate the Fundamental Periods of Existing and Instrumented RC Buildings in Thailand

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111691 - 111691

Published: Dec. 1, 2024

Language: Английский

---