Mohammad Hematibahar,Makhmud Kharun,



Data Mining,Concrete Compressive Strength,Prediction Method,Reliability,Artificial Intelligence,Machine Learning,


Concrete is the most used building material in civil engineering. The mechanical properties of concrete depend on the percentage of materials used in the mix design. There are different types of mixture methods, and the purpose of this study is to investigate the mechanical properties of concrete using the mixture method through data analysis. In this case, more than 45 mixture designs are collected to find the estimated mixture design. The estimated mixture design was found by correlation matrix and the correlation between materials of concrete. Moreover, to find the reliability of the compressive strength of concrete through data mining, two models have been established. In this term, Linear Regression (LR), Ridge Regression (RR), Support Vector Machine Regression (SVR), and Polynomial Regression (PR) have been applied to predict compressive strength. In this study, the stress-strain curve of the compressive strength of concrete was also investigated. To find the accuracy of machine learning models, Correlation Coefficient (R2), Mean Absolute Errors (MAE), and Root Mean Squared Errors (RMSE) are established. However, the machine learning prediction model of RR and PR shows the best results of prediction with R2 0.93, MAE 3.7, and RMSE 5.3 for RR. The PR R2 was more than 0.91, moreover, the stress-strain of compressive strengths has been predicted with high accuracy through Logistic Algorithm Function. The experimental results were acceptable. In the compressive strength experimental results R2 was 0.91 MAE was 1.07, and RMSE was 2.71 from prediction mixture designs. Finally, the prediction and experimental results have indicated that the current study was reliable.


I. A. Sultan A, A. Mashrei M, A. Washer G. Utilization of Wilcoxon-Mann-Whitney Statistics in Assessing the Reliability of Nondestructive Evaluation Technologies. Structures, 27, (2020), 780–7.
II. Adnan Ikram RM, A. Ewees A, Singh Parmar K, Yaseen Z, Shahid S, Kisi O. The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction, 131, (2022a), 109739.
III. Adnan Ikram RM, Dai HL, Mirshekari Chargari M, Al-Bahrani M, Mamlooki M. Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA, ; 205, (2022b ),112230.
IV. Adnan Ikram RM, Goliatt L, Kisi O, Trajkovic S, Shahid S. Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction. Mathematics, 10, 2022c, 2971.
V. Agrawal R. Sustainable design guidelines for additive manufacturing applications. Rapid Prototyping Journal, 28(7), (2022), 1221–40.
VI. Akande KO, Owolabi TO, Twaha S, Olatunji SO. Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete. IOSR J Comput Eng,16(5), (2014), 88–94.
VII. Akter S, Ali RME, Karim S, Khatun M, Alam MF. Geomorphological, Geological and Engineering Geological Aspects for Sustainable Urban Planning of Mymensingh City, Bangladesh. Open J Geol, 8:73 (2018) 7–52.
VIII. AlAlaween W, Abueed O, Gharaibeh B, Alalawin A, Mahfouf M, Alsoussi A, et al. The development of a radial based integrated network for the modelling of 3D fused deposition. Rapid Prototyping Journal, (2022) ahead-of-print(ahead-of-print).
IX. ASTM C293 / C293M – 16 Standard Test Method for Flexural Strength of Concrete (Using Simple Beam with Center-Point Loading). Vol. 2016. ASTM International. Epub ahead of print; Available from:
X. ASTM C1202-19 Standard Test Method for Electrical Indication of Concrete’s Ability to Resist Chloride. ASTM International. Epub ahead of print; 2009. Available from:

XI. Chiadighikaobi PC, Hematibahar M, Kharun M, A. Stashevskaya N, Camara K. Predicting mechanical properties of self-healing concrete with Trichoderma Reesei Fungus using machine learning. Cogent Engineering. 11(1), (2024) :2307193.
XII. Chiadighikaobi PC, Kharun M, Hematibahar M. Historical structure design method through data analysis and soft programming. Cogent Engineering, 10, 1 , (2023) 2220499.
XIII. Chopra P, KumarSharma R, Kumar M. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Advances in Materials Science and Engineering, 7648467, (2016), 10.
XIV. Dębska B. Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate. Materials, 15, (2022),8111.
XV. Erdal HI. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng Appl Artif Intell, 26, (2013), 1689–97.
XVI. Farooq F F, Czarnecki S, Niewiadomski P, Aslam F, Alabduljabbar H, Ostrowski KA, et al. A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash. Materials, 14, (2021), 4934.
XVII. Fl ̈ory S, Pottmann H. Ruled surfaces for rationalization and design in architecture. LIFE information On Responsive Information and Variations in Architecture, (2010) 103–9.
XVIII. GOST10180. Betony. Metody opredeleniya prochnosti po kontrolnym obraztsam [Concretes. Methods for determination of strength by control samples]. Vol. 36. 2013.
XIX. Hasanzadeh A, Vatin NI, Hematibahar M, Kharun M, Shooshpasha I. Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques. Materials.15, (2022), 20, 7165.
XX. Hazarika BB, Gupta D, Natarajan N. Wavelet kernel least square twin support vector regression for wind speed prediction, 29 (2022), 86320–36.
XXI. Hematibahar M, Esparham A, Vatin NI, Kharun M, Gebre TH. Effect of Gelatin Powder, Almond Shell, and Recycled Aggregates on Chemical and Mechanical Properties of Conventional Concrete. STRUCTURAL MECHANICS OF ENGINEERING CONSTRUCTIONS AND BUILDINGS. (2023); 19.

XXII. Hematibahar M, Ivanovich Vatin N, A. Alaraza H, Khalilavi A, Kharun M. The Prediction of Compressive Strength and Compressive Stress-Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm. Materials.;15 , (2022a) 19,:6975.
XXIII. Hematibahar M, Vatin NI, Alaraza HAA, Khalilavi A, Kharun M. The Prediction of Compressive Strength and Compressive Stress–Strain of Basalt Fiber Reinforced High-Performance Concrete Using Classical Programming and Logistic Map Algorithm. Materials.;19, (2022b) 15:6975.
XXIV. Hsieh SC. Prediction of Compressive Strength of Concrete and Rock Using an Elementary Instance-Based Learning Algorithm. Advances in Civil Engineering, (2021),10.
XXV. J. Alghamdi S. Classifying High Strength Concrete Mix Design Methods Using Decision Trees. Materials, 15, (2022) 1950.
XXVI. Kaewunruen S, Sresakoolchai J, Huang J, Zhu Y, Ngamkhanong C, M. Remennikov A. Machine Learning Based Design of Railway Prestressed Concrete Sleepers. Appl Sci.12, (2022), 10311.
XXVII. Kashyzadeh KR, Amiri N, Ghorban S, Souri K. Prediction of Concrete Compressive Strength Using a Back-Propagation Neural Network Optimized by a Genetic Algorithm and Response Surface Analysis Considering the Appearance of Aggregates and Curing Conditions. Buildings.;12 , (2022) ,438.
XXVIII. Khan MA, Memon SA, Farooq F, Javed MF, Aslam F, Alyousef R. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Adv Civ Eng, 2021, 6618407.
XXIX. Khorasani M, Loy J, Ghasemi A, Sharabian E, Leary M, Mirafzal H, et al. A review of Industry 4.0 and additive manufacturing synergy. Rapid Prototyping Journal, 28, 8 (2022) , 1462–75.
XXX. Kumar A, Harish CA, Raj Kapoor N, Mazin AM, Kumar K, Majumdar A, et al. Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models. Sustainability, 14, (2022), 2404.
XXXI. Liu Y. High-Performance Concrete Strength Prediction Based on Machine Learning. Hindawi.; (2022), 5802217,7.
XXXII. Mao F, Zhao X, Ma P, Chi S, Richards K, M. Hannah D, et al. Revision of biological indices for aquatic systems: A ridge-regression solution. Ecological Indicators, 106, (2019),105478.
XXXIII. Nafees A, Khan S, Javed MF, Alrowais R, Mohamed AM, Vatin NI. Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF. Polymers,14, (2022),1538.
XXXIV. Nguyen KT, Nguyen QD, Le TA, Shin J, Lee K. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr Build Mater, 247, (2020), 118581.
XXXV. Peng X, Zhuang Z, Yang Q. Predictive Modeling of Compressive Strength for Concrete at Super Early Age. Materials, 15, (2022), 4914.
XXXVI. Riener C, Schabert R. Linear slices of hyperbolic polynomials and positivity of symmetric polynomial functions. arXiv preprint.
XXXVII. Sami Ullah H, Khushnood RA, Farooq F, Ahmad J, Vatin NI, Zakaria Ewais DY. Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches. Materials, 15, (2022)3166.
XXXVIII. Shahmansouri AA, Yazdani M, Hosseini M, Akbarzadeh Bengar H, Farrokh Ghatte H. The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network. Construction and Building Materials, 317, (2022), 125876.
XXXIX. Shen Z, Farouk Deifalla A, Kaminski P, Dyczko A. Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning. Materials, 15, (2022) 3523.
XL. Son J, Yang S. A New Approach to Machine Learning Model Development for Prediction of Concrete Fatigue Life under Uniaxial Compression. Appl Sci,12 (2022),9766.
XLI. Topçu İB, Sarıdemir M. Prediction of properties of waste AAC aggregate concrete using artificial neural network. Computational Materials Science, 41, 1, (2007) 117–25.
XLII. Vakharia V, Gujar R. Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques. Construction and Building Materials,225 , (2019), 292–301.
XLIII. Yang D, Yan C, Liu S, Jia Z, Wang C. Prediction of Concrete Compressive Strength in Saline Soil Environments. Materials, 15, (2022), 4663.
XLIV. Zheng X, Peng X, Zhao J, Wang X. Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data. Appl Sci, 12, (2022), 11905.

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