Prediction of the compressive strength of strain‐hardening cement‐based composites using soft computing models DOI

Peshkawt Yaseen Saleh,

Dilshad Kakasor Ismael Jaf, Aso A. Abdalla

и другие.

Structural Concrete, Год журнала: 2023, Номер 24(5), С. 6761 - 6777

Опубликована: Июнь 1, 2023

Abstract Strain‐hardening cement‐based composites or engineered cementitious (ECC) is concrete produced using randomly distributed short polymer fibers. It very ductile compared to conventional concrete. Compressive strength (CS) a critical property used as quality control tool evaluate the of implemented in structural provisions and mix designs. Accordingly, save cost time for testing, it essential provide predictive model forecast CS mixtures machine learning modeling techniques. In this study, different tools are propose analytical models predict ECC mixtures, such linear regression (LR), multi‐expression programming (MEP), artificial neural network (ANN), Gaussian process (GPR). A total 210 data were collected from literature train test developed model. The fly ash‐to‐cement ratio ranged 0 5.6, water binder 0.19 0.56, superplasticizer fiber content, curing times 1 180 days. Based on evaluation models, ANN superior other with high coefficient determination ( R 2 ), root mean squared error (RMSE), absolute (MAE), scatter index (SI). sensitivity analysis input parameters' effect prediction indicates that forecasting ECC's CS.

Язык: Английский

Prediction of concrete materials compressive strength using surrogate models DOI

Wael Emad,

Ahmed Salih Mohammed, Rawaz Kurda

и другие.

Structures, Год журнала: 2022, Номер 46, С. 1243 - 1267

Опубликована: Ноя. 10, 2022

Язык: Английский

Процитировано

192

Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures DOI

Wael Emad,

Ahmed Salih Mohammed, Ana Brás

и другие.

Construction and Building Materials, Год журнала: 2022, Номер 349, С. 128737 - 128737

Опубликована: Авг. 18, 2022

Язык: Английский

Процитировано

100

A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns DOI
Abidhan Bardhan, Rahul Biswas, Navid Kardani

и другие.

Construction and Building Materials, Год журнала: 2022, Номер 337, С. 127454 - 127454

Опубликована: Май 1, 2022

Язык: Английский

Процитировано

94

Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data DOI
Panagiotis G. Asteris, Μαρία Καρόγλου,

Athanasia D. Skentou

и другие.

Ultrasonics, Год журнала: 2024, Номер 141, С. 107347 - 107347

Опубликована: Май 20, 2024

Язык: Английский

Процитировано

69

Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models DOI
Hai‐Van Thi, May Huu Nguyen,

Son Hoang Trinh

и другие.

Construction and Building Materials, Год журнала: 2023, Номер 369, С. 130613 - 130613

Опубликована: Фев. 9, 2023

Язык: Английский

Процитировано

44

Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete DOI Creative Commons
Dilshad Kakasor Ismael Jaf, Aso A. Abdalla, Ahmed Salih Mohammed

и другие.

Heliyon, Год журнала: 2024, Номер 10(4), С. e25997 - e25997

Опубликована: Фев. 1, 2024

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it not biodegradable and causes environmental issues. One of the most effective ways recycling those wastes or using them as replacement for normal aggregate in concrete mixture, which has high impact resistance toughness; thus, will good choice. In study, 135 data were collected from previous literature develop model prediction rubberized compressive strength; database comprised different mixture proportions, maximum size (1-40 mm), percentage (0-100%) replacing natural fine coarse aggregates among input parameters addition cement content (380-500 kg/m

Язык: Английский

Процитировано

26

Predicting the web crippling capacity of cold-formed steel lipped channels using hybrid machine learning techniques DOI Creative Commons
Ramy I. Shahin, Mizan Ahmed, Qing Quan Liang

и другие.

Engineering Structures, Год журнала: 2024, Номер 309, С. 118061 - 118061

Опубликована: Апрель 22, 2024

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied the of section. These induce buckling and distortion in web, ultimately leading member's collapse. It is challenging task accurately determine crippling capacity CFSL channel due its complexity various influencing factors. This paper presents hybrid soft computing techniques for predicting subjected two flange load cases. The developed combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) Particle Swarm Optimization (PSO) improve computational efficiency accuracy. finite element models validated experimental results then employed generate database, which used train machine learning models, including ANN, GA-ANN, PSO-ANN. Analysis undertaken on reliability existing design formulas determining channels. shown that PSO-ANN model outperforms other terms prediction codes not reliable estimating However, proposed yields good correlation analysis results. A user- friendly graphical interface tool practical cold-formed steel lipped

Язык: Английский

Процитировано

18

Revealing the nature of soil liquefaction using machine learning DOI Creative Commons
Sufyan Ghani, Ishwor Thapa,

Amrendra Kumar

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 21, 2025

Язык: Английский

Процитировано

3

Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading DOI
Chen Wang, Tak–Ming Chan

Engineering Structures, Год журнала: 2022, Номер 276, С. 115392 - 115392

Опубликована: Дек. 12, 2022

Язык: Английский

Процитировано

53

Prediction of the load-shortening curve of CFST columns using ANN-based models DOI
Mohammadreza Zarringol, Huu‐Tai Thai

Journal of Building Engineering, Год журнала: 2022, Номер 51, С. 104279 - 104279

Опубликована: Фев. 26, 2022

Язык: Английский

Процитировано

43