Comparative analysis of sloshing effects on elevated water tanks’ dynamic response using ANN and MARS

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: Jan. 14, 2025

Important infrastructure elements like elevated water tanks are vulnerable to fluid sloshing brought on by earthquakes, which can seriously harm the structure. In order precisely capture fluid–structure interaction, this work uses a 3D finite element model based Coupled Eulerian–Lagrangian (CEL) approach investigate dynamic response of raised under seismic excitation. Seismic ground motions were applied model, and displacements, accelerations, stresses that resulted examined. The results highlight how significantly affects tank because waves increase structural deformations imposing large hydrodynamic pressures walls. tank's performance as tuned liquid damper (TLD) was also evaluated in study. Multivariate Adaptive Regression Splines (MARS) Artificial Neural Network (ANN) models created trained further examine forecast response. According statistical analysis Taylor diagram evaluation, ANN outperformed MARS forecasting displacement numerical simulations precise made possible use ABAQUS software, potent tool. study's conclusions be used improve construction reduce risk. impact different geometries, characteristics, may investigated future studies. This research innovatively employs software simulate stabilizing effect steel structures during earthquakes. By optimizing levels 60% capacity, study explores potential damping mechanism mitigate vibrations. comparison empty tanks, leads better throughout velocity, acceleration, responses, speeds up stabilization lowers loads. demonstrates well adjusted dampers (TLDs) vibrations tanks.

Language: Английский

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Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 21, 2025

Language: Английский

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Comparison of experimental and analytical studies in light gauge steel sections on CFST using SFRC in beams subjected to high temperatures

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

Language: Английский

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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability

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

Published: March 8, 2025

Abstract The rapid increase in global waste production, particularly Polymer wastes, poses significant environmental challenges because of its nonbiodegradable nature and harmful effects on both vegetation aquatic life. To address this issue, innovative construction approaches have emerged, such as repurposing Polymers into building materials. This study explores the development eco-friendly bricks incorporating cement, fly ash, M sand, polypropylene (PP) fibers derived from Polymers. primary innovation lies leveraging advanced machine learning techniques, namely, artificial neural networks (ANN), support vector machines (SVM), Random Forest AdaBoost to predict compressive strength these Polymer-infused bricks. polymer bricks’ was recorded output parameter, with PP waste, age serving input parameters. Machine models often function black boxes, thereby providing limited interpretability; however, our approach addresses limitation by employing SHapley Additive exPlanations (SHAP) interpretation method. enables us explain influence different variables predicted outcomes, thus making more transparent explainable. performance each model evaluated rigorously using various metrics, including Taylor diagrams accuracy matrices. Among compared models, ANN RF demonstrated superior which is close agreement experimental results. achieves R 2 values 0.99674 0.99576 training testing respectively, whereas RMSE value 0.0151 (Training) 0.01915 (Testing). underscores reliability estimating strength. Age, ash were found be most important variable predicting determined through SHAP analysis. not only highlights potential enhance predictive for sustainable materials demonstrates a novel application improve interpretability context repurposing.

Language: Английский

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Estimation of the Compressive Strength of Ultrahigh Performance Concrete using Machine Learning Models

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 25, P. 200471 - 200471

Published: Dec. 25, 2024

Language: Английский

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Review of seismic evaluation frameworks for highway bridges based on life cycle concepts

Structure and Infrastructure Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 23, 2025

Language: Английский

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Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 33

Published: March 25, 2025

Self-compacting concrete (SCC) has become increasingly popular due to its superior workability, segregation resistance, and compressive strength. As the traditional methods for strength prediction are costly time-intensive, this study explores machine learning (ML) techniques as efficient alternatives SCC prediction. Three state-of-the-art hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) models, optimised using Firefly Algorithm (FA), Particle Swarm Optimization (PSO) Genetic (GA). For purpose, a robust dataset of 366 instances 7 input parameters is taken from literature. After data analysis pre-processing, hyperparameters models tuned best-fit model tested on unforeseen data. ANFIS-FF stands out best-performing (RTR2 = 0.945 RTS2 0.9395) in both training testing phases, closely followed by ANFIS-GA. All outperform ANFIS model, outlining significance hybridisation, however, ANFIS-PSO lags behind other two models. The highlights importance integrating with metaheuristic algorithms tackling complex engineering problems like design optimal mix design, minimising material waste ensuring cost-effectiveness. It serves benchmark future research comparing hybridisation starting point ANFIS.

Language: Английский

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Investigating the Impact of Biaxial Geogrid Reinforcement on Subgrade Soil Strength Enhancement: A Machine Learning Analysis Using the MARS Model

Indian geotechnical journal, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

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Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Language: Английский

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