MACHINE LEARNING MODELS FOR PREDICTING THE COMPRESSIVE STRENGTH OF CONCRETE WITH SHREDDED PET BOTTLES AND M-SAND AS FINE AGGREGATE - Research findings on DT vs MLR vs ANN

Aug 5, 2026

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Machine learning and artificial intelligence are used to build self-trained prediction systems from historical data. This study applies machine learning algorithms to predict the compressive strength of grade 30 concrete using shredded PET bottles and M-sand as fine aggregates. Concrete specimens are prepared with PET volume fractions from 0% to 2% (0.5% steps) and M-sand contents of 25%, 50%, 75%, and 100%, then cured for 7, 28, and 90 days. Multiple Linear Regression, Artificial Neural Network, and Decision Tree models are evaluated.

LanguageEnglish

CategoryResearch & Report

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Abstract

Materials and experimental setup

Machine learning models and evaluation

Brought to you by INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA Document typeArticleSource typeJournalISSN1511788XDOI Related documents # The Relation of CompressionStrength with Modulus ofRupture and UPV of ConcreteContaining M-sand as FineAggregate 10.31436/IIUMEJ.V26I1.2998View more v Nadimalla,A.,Masjuki,S.A.,Saad,S.A. # MACHINE LEARNING MODELS FOR PREDICTING THECOMPRESSIVE STRENGTH OF CONCRETE WITHSHREDDED PET BOTTLES AND M-SAND AS FINEAGGREGATE (2020)Advances in Science,Technology and EngineeringSystems Prediction of compressive strength of concrete NADIMALLA,ALTAMASHUDDINKHANa,b;MASJUKI,SITI ALIYYAH ;GUBBI,ABDULLAHC;KHAN,AN]UMd;MOKASHI,IMRANe Varma,M.S.,Jain,A.,Hemanth, B. (2023)AIP Conference Save all to author list Proceedings Department of Civil Engineering,International Islamic University Malaysia),Gombak,MalaysiaDepartment of Civil Engineering,Bearys Institute of Technology,Mangalore,India Application of AI models forpredicting properties of mortarsincorporating waste powdersunder Freeze-Thaw condition Department of Electronics and Communication,Bearys Institute of Technology,Mangalore,IndiadDepartment of Basic Science,Bearys Institute of Technology,Mangalore,IndiaView additional affiliations Cihan,M.T.,Aral,I.F(2022)Computers and Concrete Full text options ∨ Export View all related documentsbased on references Find more related documents inScopus based on: Abstract Author keywords Authors>Keywords> SciVal Topics Abstract Machine Learning (ML)and Artificial Intelligence (AI)are closely intertwined and represent the latest cutting-edgetechnologies that facilitate the development of intelligent prototypes.Machine learning is a critical subset of AI thatdeliberates the development of self-trained algorithms that use previous databases and analysis for resultpredictions.By leveraging past data,machine learning empowers computers to make predictions and decisions.Thisstudy investigates the use of MLalgorithms to predict the compressive strength of grade 30 concrete,incorporatingshredded PET bottles and M-sand as fine aggregates.The experimental setup involved preparing concrete specimens with shredded PET bottle aggregates,varying the volume from o%to 2%in increments of o.5%.Differentpercentages of M-sand were incorporated at 25%,50%,75%,and 100%.The mixing proportions adhered to thestandards defined by the Department of Environment(DOE).Cubic specimens were cast and cured for 7,28,and 90days.The study employs Multiple Linear Regression(MLR),Artificial Neural Network (ANN),and Decision Tree(DT)models,using the experimental data for predictive analysis.The evaluation of the three models for predictingcompressive strength yielded interesting results:The Decision Tree(DT)model demonstrated the best performance,with a relatively low Mean Squared Error (MSE)of 5.125 and Mean Absolute Error(MAE)of 1.642 and a high R²valueof o.918,indicating that the model explains approximately 91.8%of the variance in the target variable.The DTmodel's ability to handle complex,non-linear data relationships made it particularly effective in evaluating concretestrength.The Multiple Linear Regression(MLR)model provided reasonable predictions but showed higher errorscompared to the DT model,with MSE and MAE values of 26.663 and 4.298,respectively,and an R²score of o.571,demonstrating a moderate ability to explain the variance in the data.Conversely,the Artificial Neural Network(ANN)model exhibited the least accuracy,with the highest errors(MSE of112.33 and MAE of 8.52)and a negative R²score(-0.64),indicating poor model training and an inability to capture the relationships between parameterseffectively,partly due to the relatively small dataset.The

FAQs

研究使用哪些机器学习模型预测混凝土抗压强度？

研究对比了多元线性回归（MLR）、人工神经网络（ANN）与决策树（DT）三种模型，利用实验数据进行预测分析。

混凝土试件的配合与变量如何设置？

试件以碎PET作为骨料，PET体积分数在0%到2%之间按0.5%递增；同时设置M-sand比例为25%、50%、75%与100%。混凝土按标准进行拌制，并在7、28与90天进行养护。

三种模型的预测表现如何？

决策树模型表现最好，具有较低MSE与MAE并获得较高R²，说明其能较好解释目标变量的方差。MLR能给出合理预测但误差更高；ANN在该数据规模下准确性较差，出现负的R²。

Aug 5, 2026

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