Improvement of compressive strength prediction accuracy for concrete is crucial and considered a challenging task to reduce costly experiments time. Particularly, the determination using ground granulated blast furnace slag (GGBFS) more difficult due complexity composition mix design. In this paper, an approach random forest (RF), which one powerful machine learning algorithms, proposed predicting GGBFS. The RF model first evaluated determine best architecture, constitutes 500 growth trees...

The prediction accuracy of concrete compressive strength is important and considered a challenging task, aiming at reducing costly time‐consuming experiments. Moreover, using blast‐furnace slag (BFS) fly ash (FA) more difficult due to the complex mix design composition. In this investigation, an approach artificial neuron network (ANN), one most powerful machine learning algorithms, applied predict containing BFS FA. ANN models with hidden layer 13 number cases are proposed determine best...

Abstract Understanding and predicting concrete carbonation are significant in designing durability maintaining the service life of reinforced structures. However, this purpose can hardly be reached because complex mechanisms depending on various variables such as cement content (C), fly ash (FA), water (W), concentration CO 2 , relative humidity (RH), temperature (T°C), exposition time (Time). This investigation proposes a machine learning (ML) approach including eight ML algorithms four...

This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and descent (ANN-GD). A database containing 106 results RC beam tests is collected used investigate performance proposed algorithms. The ANN phase uses 70% data, randomly taken from dataset, whereas remaining 30% data are for algorithms’...

Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time‐consuming experiments. Notably, determination later‐age more difficult due time required perform Therefore, predicting crucial in specific applications. In this investigation, approach using a feedforward neural network (FNN) machine learning algorithm was proposed predict concrete. The model fully evaluated terms performance capability over statistical results 1000 simulations...

Accurate measurement of the critical buckling stress is crucial in entire field structural engineering. In this paper, load Y-shaped cross-section steel columns was predicted by Artificial Neural Network (ANN) using Levenberg-Marquardt algorithm. The results 57 tests were used to generate training and testing datasets. Seven input variables considered, including column length, width, equal angles thickness, width thickness welded plate, total deviations following Ox Oy directions. output...

An extensive simulation program is used in this study to discover the best ANN model for predicting compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish purpose, an experimental database 595 samples compiled from literature and utilized find architecture. The cement content, water coarse aggregate fine GGBFS carboxylic type hyper plasticizing superplasticizer testing age are eight inputs database. As a result, optimal selection design...

Basalt Fiber Asphalt Concrete (BFAC) is an environmentally friendly and durable material with potential road, bridge, infrastructure construction applications. This study investigates the application of Machine Learning (ML) models, specifically classical Gradient Boosting (CGB) algorithm, in conjunction metaheuristic algorithms, to predict Marshall Stability (MS) optimize design BFAC mixtures. The model trained tested on a comprehensive dataset experimental samples, taking into account...

Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the construction and transportation industries due to its numerous advantages, such as ease of building challenging sites, noise reduction, enhanced tensile strength, bending decreased structural cracking. Traditional methods for assessing compressive strength SCCRF are generally time-consuming expensive, necessitating development a model forecast strength. This research aimed predict CS using Extreme Gradient...

Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As result, SCC widely used in construction, especially at locations where structures are difficult construct. Filling ability one of three basic requirements that must be met when designing mix. The slump flow (SF) determine mixture's filling capacity. it critical estimate this number fast precisely. purpose study propose...

Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As result, SCC widely used in construction, especially at locations where structures are difficult construct. Filling ability one of three basic requirements that must be met when designing mix. The slump flow (SF) determine mixture's filling capacity. it critical estimate this number fast precisely. purpose study propose...

Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As result, SCC widely used in construction, especially at locations where structures are difficult construct. Filling ability one of three basic requirements that must be met when designing mix. The slump flow (SF) determine mixture's filling capacity. it critical estimate this number fast precisely. purpose study propose...