Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge using such structure lies selection many parameters constituting CFST, which necessitates defining complex relationships between components and corresponding properties. capacity (Pu) CFST is among most important mechanical In this study, possibility feedforward neural network (FNN) to predict Pu was investigated. Furthermore, an...

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...

Soil cohesion (C) is one of the critical soil properties and closely related to basic such as particle size distribution, pore size, shear strength. Hence, it mainly determined by experimental methods. However, methods are often time‐consuming costly. Therefore, developing an alternative approach based on machine learning (ML) techniques solve this problem highly recommended. In study, models, namely, support vector (SVM), Gaussian regression process (GPR), random forest (RF), were built a...

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...

In the design process of foundations, pavements, retaining walls, and other geotechnical matters, estimation soil strength-related parameters is crucial. particular, friction angle a critical shear strength factor in assessing stability deformation structures. Practically, laboratory or field tests have been conducted to determine soil. However, these jobs are often time-consuming quite expensive. Therefore, prediction geo-mechanical properties soils using machine learning techniques has...

In this study, a novel hybrid surrogate machine learning model based on feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas OSS used optimize weights bias FNN for developing (FNN-OSS). For achieving goal, an experimental database containing 422 instances firstly gathered from literature develop FNN-OSS algorithm. The input variables in contained geometrical...

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’...

In this paper, an extensive simulation program is conducted to find out the optimal ANN model predict shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and reinforcements. For acquiring purpose, experimental database 125 samples collected from literature used best architecture ANN. database, input variables consist 9 inputs, such as ratio beam width, effective depth, span compressive concrete, longitudinal FRP reinforcement ratio, modulus elasticity...

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...

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is combination balancing composite motion optimization (BCMO) - very new technique and artificial neural network (ANN). For aim, an experimental database consisting 422 datasets used for development validation ANN-BCMO model. Variables in are related with geometrical characteristics structural members, mechanical properties constituent...

In this paper, the main objectives are to investigate and select most suitable parameters used in particle swarm optimization (PSO), namely number of rules (nrule), population size (npop), initial weight (wini), personal learning coefficient (c1), global (c2), velocity limits (fv), order improve performance adaptive neuro-fuzzy inference system determining buckling capacity circular opening steel beams. This is an important mechanical property terms safety structures under subjected loads....

The main objective of the present study is to apply Artificial Neural Network (ANN), which one most popular machine learning models, accurately predict soil unconfined compressive strength (qu) for use in designing foundations civil engineering structures. For development model, data 118 samples were collected from Long Phu 1 power plant project, Soc Trang Province, Vietnam. database physicomechanical properties soils was prepared model study, where 70% used training and 30% testing model....

Castellated steel beams (CSB) are an attractive option for the construction industry thanks to outstanding advantages, such as ability exceed large span, lightweight, and allowing flexible arrangement of technical pipes through beams. In addition, complex localized global failures characterizing these structural members have led researchers focus on development efficient design guidelines. This paper aims propose artificial neural network (ANN) model with optimal architecture predict...

This study aims to investigate the influence of all mixture components high-performance concrete (HPC) on its early compressive strength, ranging from 1 14 days. To this purpose, a Gaussian Process Regression (GPR) algorithm was first constructed using database gathered available literature. The included contents cement, blast furnace slag (BFS), fly ash (FA), water, superplasticizer, coarse, fine aggregates, and testing age as input variables predict output problem, which strength. Several...

The shear strength of corroded reinforced concrete (CRC) beams is a critical consideration during the design stages RC structures. In this study, we propose machine learning technique for estimating CRC across range service periods. To do this, gathered 158 beam tests and used Artificial Neural Network (ANN) to create forecast model considered output. Twelve input variables indicate geometrical material properties, reinforcing parameters, degree corrosion in beam, whereas output considered....

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...