The cementitious composites have different properties in the changing environment. Thus, knowing their mechanical is very important for safety reasons. most case of concrete Compressive strength (CS). To predict CS Machine learning (ML) approaches has been essential. This study includes collection data from experimental work and application ML techniques to containing fly ash. chemical physical all materials used this were evaluated. Although, emphasis research on use supervised machine...

Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties concrete. This study uses ensemble random forest (RF) gene expression programming (GEP) compressive strength high The parameters include cement content, coarse aggregate to fine ratio, water, superplasticizer. Moreover, statistical analyses like MAE, RSE, RRMSE are used evaluate performance models. RF model outbursts in as it a weak base learner decision tree gives adamant...

Concrete is a widely used construction material, and cement its main constituent. Production utilization of severely affect the environment due to emission various gases. The application geopolymer concrete plays vital role in reducing this flaw. This study supervised machine learning algorithms, decision tree (DT), bagging regressor (BR), AdaBoost (AR) estimate compressive strength fly ash-based concrete. coefficient determination (R2), mean absolute error, square root error were evaluate...

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on comparison between individuals and ensemble approaches, such as bagging. Optimization bagging done by making 20 sub-models to depict accurate one. Variables like cement content, fine coarse aggregate, water, binder-to-water ratio, fly-ash, superplasticizer modeling. Model performance evaluated various statistical indicators mean absolute error (MAE), square...

The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based have been utilized predict mechanical behaviour HSC. data be used in modelling consist several input parameters such as cement, water, fine aggregate, coarse aggregate combination with a superplasticizer. Empirical relation mathematical expression has proposed using engineering programming. efficiency...

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 experimental procedures, soft computing techniques, namely, random forest regression (RFR) gene expression programming (GEP), are used this study to develop an empirical model for prediction compressive strength FGPC. A widespread, reliable, consistent database FGPC set up via comprehensive literature review. The...

Recently, the addition of natural fibers to high strength concrete (HSC) has been great interest in field construction materials. Compared artificial fibers, are cheap and locally available. Among all coconut have greatest known toughness. In this work, mechanical properties fiber reinforced (CFR-HSC) explored. Silica fume (10% by mass) super plasticizer (1% also added CFR-HSC. The influence 25 mm-, 50 75 mm-long 0.5%, 1%, 1.5%, 2% contents mass is investigated. microstructure CFR-HSC...

To minimize the environmental risks and for sustainable development, utilization of recycled aggregate (RA) is gaining popularity all over world. The use coarse (RCA) in concrete an effective way to pollution. RCA does not gain more attraction because availability adhered mortar on its surface, which poses a harmful effect properties concrete. However, suitable mix design enables it reach targeted strength be applicable wide range construction projects. achievement from proposed at...

Compressive strength is one of the important property concrete and depends on many factors. Most compressive predictive models mainly rely available literature data, which are too simple to consider all contributing This study adopted a new approach predict sugarcane bagasse ash (SCBAC). A vast amount data from fifteen laboratory tested samples with different dosage ash, were respectively used calibrate validate models. The novel Gene Expression Programming, Multiple Linear Regression...

Silica fume (SF) is a mineral additive that widely used in the construction industry when producing sustainable concrete. The integration of SF concrete as partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, development predictive machine learning models critical. Thus, this study aims create modelling tools estimating compressive...

Abstract The disposal of waste from coal plants and glass (WG) is causing significant environmental problems all around the planet. Currently, amount discarding these wastes increased. One possible option to utilize fly ash (FA) as a partial substitute for cement EG fractional sand in concrete, respectively. Besides, fibers can improve strength durability concrete; explicitly speaking, using coconut (CFs) trend due their highest toughness among natural making it suitable material fiber...

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. application supervised machine learning (ML) algorithms to forecast mechanical properties has significant developing innovative environment field civil engineering. This study was based on use artificial neural network (ANN), boosting, and AdaBoost ML approaches, python coding predict compressive strength (CS) high...

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes damage to the environment. Rapid increases in population demand for construction throughout world lead a significant deterioration or reduction natural resources. Meanwhile, waste continues grow at high rate as older buildings are destroyed demolished. As result, use of recycled materials may contribute improving quality life preventing environmental damage. Additionally, application...

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), neural networks (ANNs), and gene expression programming (GEP) to develop empirical models for predicting the compressive strength (CS) slump values of fly ash-based geopolymer concrete. A database 245 CS 108 were established from published literature, where 17 significant parameters chosen as input variables development models. The trained tested using statistical measures...

The complication linked with the prediction of ultimate capacity concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses this member subjected to axial-load only. distinguishing feature gene expression programming (GEP) has been utilized establishing model axial behavior long CFST. proposed equation correlates CFST depth, thickness, yield strength steel, compressive concrete and length CFST, without any expensive...

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, decision tree to estimate compressive strength containing supplementary cementitious materials (fly ash blast furnace slag). performance models was compared assessed using coefficient determination (R2), mean absolute error, square root error. model further validated...

Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used approaches. neural network (ANN) support vector (SVM) gene expression programming (GEP) consisting 300 datasets have utilized model to foresee mechanical property SCC. Data modeling consist several parameters such cement, water–binder ratio, coarse...