Prediction of Compressive Strength for Fly Ash-Based Concrete: Critical Comparison of Machine Learning Algorithms

Document Type : Regular Article


1 Graduate Students, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

2 Ph.D. Student, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

3 Assistant Professor, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India


In the construction field, compressive strength is one of the most critical parameters of concrete. However, a significant amount of physical effort and natural raw materials are required to produce concrete. In addition, the curing period of concrete for at least 28 days is a must for attaining the required compressive strength. Various types of industrial and agricultural wastes have been used in concrete to reduce cement consumption and problems due to its production. Therefore, considering such constraints, the application of Artificial Intelligence (AI) has been widely used in the current scenarios to predict the desired output parameters. In the present study, 12 input parameters have been considered along with 455 data points and nine Machine Learning (ML) models to forecast the compressive strength of Fly Ash (FA) based concrete. The output from the models has been compared to find the best-fit model in terms of numerous analyses such as visual descriptive statistics, errors, R2, Taylor’s diagram, Feature Importance (FI), and scatter plots. Based on the analysis of the current study, Decision Tree (DT) and Gradient Boost (GB) were found to be the best-fit model because of the least errors and higher R2 values as compared to other models.


Main Subjects

[1]     Schwab K. The Fourth Industrial Revolution: what it means, how to respond. World Econ Fourm 2016.
[2]     Khan R, Vyas H. a Study of Impact of Brick Industries on Environment and Human Health in Ujjain City ( India ). J Environ Res Dev 2008;2:421–5.
[3]     Pearson TH, Rosenberg R. A Comparitive Study of the Effects of the Marine Environment of Wastes from Cellulose Industries in Scotland and Sweden 2008;5:77–9.
[4]     Bildirici ME. Cement production , environmental pollution , and economic growth : evidence from China and USA. Clean Technol Environ Policy 2019.
[5]     Benhelal E, Zahedi G, Shamsaei E, Bahadori A. Global strategies and potentials to curb CO2 emissions in cement industry. J Clean Prod 2013;51:142–61.
[6]     MacLaren DC, White MA. Cement: Its chemistry and properties. J Chem Educ 2003;80:623–35.
[7]     Hanifa M, Agarwal R, Sharma U, Thapliyal PC, Singh LP. A review on CO2 capture and sequestration in the construction industry: Emerging approaches and commercialised technologies. J CO2 Util 2023;67:102292.
[8]     Ren M, Ma T, Fang C, Liu X, Guo C, Zhang S, et al. Negative emission technology is key to decarbonizing China’s cement industry. Appl Energy 2023;329:120254.
[9]     Verma YK, Ghime D, Mazumdar B, Ghosh P. Emission reduction through process integration and exploration of alternatives for sustainable clinker manufacturing. Int J Environ Sci Technol 2023.
[10]   Singh R, Sohal KS. Application of Waste Bones in Civil Engineering Practices. Sustain. Dev. Through Eng. Innov., 2021, p. 321–32.
[11]   Kapoor K, Singh SP, Singh B. Permeability of self-compacting concrete made with recycled concrete aggregates and Portland cement-fly ash-silica fume binder. J Sustain Cem Mater 2021;10:213–39.
[12]   Shrivas A, Jain D. Application of Different Waste in Concrete as a Partial Replacement of Cement Coarse Aggregate. Int J Sci Technol Eng 2015;2:89–107.
[13]   Guney Y, Sari YD, Yalcin M, Tuncan A, Donmez S. Re-usage of waste foundry sand in high-strength concrete. Waste Manag 2010;30:1705–13.
[14]   Meyer C. Aerated Concrete as a Green Building Material. Aerated Concr 2006;1:10.
[15]   Gopalakrishna B, Dinakar P. Mix design development of fly ash-GGBS based recycled aggregate geopolymer concrete. J Build Eng 2023;63:105551.
[16]   Song Y, Zhao J, Ostrowski KA, Javed MF, Ahmad A, Khan MI, et al. Prediction of compressive strength of fly-ash-based concrete using ensemble and non-ensemble supervised machine-learning approaches. Appl Sci 2022;12.
[17]   Gardner L, Yun X, Walport F. The Continuous Strength Method – Review and outlook. Eng Struct 2023;275:114924.
[18]   Faried AS, Mostafa SA, Tayeh BA, Tawfik TA. Mechanical and durability properties of ultra-high performance concrete incorporated with various nano waste materials under different curing conditions. J Build Eng 2021;43:102569.
[19]   Sandhu RK, Siddique R. Influence of rice husk ash (RHA) on the properties of self-compacting concrete: A review. Constr Build Mater 2017;153:751–64.
[20]   Sohal KS, Singh R. Sustainable Use of Sugarcane Bagasse Ash in Concrete Production. In: Singh H, Cheema PPS, Garg P, editors. Sustain. Dev. Through Eng. Innov., 2020, p. 397–409.
[21]   Kolawole JT, Babafemi AJ, Fanijo E, Chandra Paul S, Combrinck R. State-of-the-art review on the use of sugarcane bagasse ash in cementitious materials. Cem Concr Compos 2021;118:103975.
[22]   Singh R, Patel M. Investigating the Effect of Corn Cob Ash on the Characteristics of Cement Paste and Concrete: A Review. Environ. Concerns Remediat., Cham: Springer International Publishing; 2022, p. 91–103.
[23]   Adesanya DA. Evaluation of blended cement mortar , concrete and stabilized earth made from ordinary Portland cement and corn cob ash. Constr Build Mater Vol 1996;10:451–6.
[24]   Singh R, Sodhi AK, Bhanot N. Sustainable Concrete Production by Integrating Wastes : A Comparative Study with and Without Bacillus Megaterium. Int. Conf. Sustain. Waste Manag. through Des., vol. 1, Springer International Publishing; 2019, p. 377–85.
[25]   Sohal KS, Kaur I, Singh R. Use of Electric Arc Furnace Dust in Concrete: A Review. Sustain. Waste Manag. through Des., 2019, p. 464–72.
[26]   Sodhi AK, Bhanot N, Singh R, Alkahtani M. Effect of integrating industrial and agricultural wastes on concrete performance with and without microbial activity. Environ Sci Pollut Res 2021:1–17.
[27]   Singh R, Patel M. Strength and durability performance of rice straw ash-based concrete: an approach for the valorization of agriculture waste. Int J Environ Sci Technol 2022.
[28]   Ankur N, Singh N. Performance of cement mortars and concretes containing coal bottom ash: A comprehensive review. Renew Sustain Energy Rev 2021;149:111361.
[29]   Sua-Iam G, Makul N. Utilization of coal- and biomass-fired ash in the production of self-consolidating concrete: A literature review. J Clean Prod 2015;100:59–76.
[30]   Singh R, Patel M, Sohal KS. The Potential Use of Waste Paper Sludge for Sustainable Production of Concrete—A Review. Recent Adv. Civ. Eng., 2022, p. 365–74.
[31]   Bui NK, Satomi T, Takahashi H. Influence of industrial by-products and waste paper sludge ash on properties of recycled aggregate concrete. J Clean Prod 2019;214:403–18.
[32]   Williams A, Markandeya A, Stetsko Y, Riding K, Zayed A. Cracking potential and temperature sensitivity of metakaolin concrete. Constr Build Mater 2016;120:172–80.
[33]   Dede T, Kankal M, Vosoughi AR, Grzywiński M, Kripka M. Artificial Intelligence Applications in Civil Engineering. Adv Civ Eng 2019;2019.
[34]   Minsky M. Steps Toward Artificial Intelligence. Proc IRE 1961;49:8–30.
[35]   Lu P, Chen S, Zheng Y. Artificial Intelligence in Civil Engineering. Math Probl Eng 2012;2012:1–22.
[36]   Marani A, Nehdi ML. Machine learning prediction of compressive strength for phase change materials integrated cementitious composites. Constr Build Mater 2020;265:120286.
[37]   Ahmad J, Zaid O, Siddique MS, Aslam F, Alabduljabbar H, Khedher KM. Mechanical and durability characteristics of sustainable coconut fibers reinforced concrete with incorporation of marble powder. Mater Res Express 2021;8.
[38]   Farooq F, Amin MN, Khan K, Sadiq MR, Javed MF, Aslam F, et al. A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC). Appl Sci 2020;10:1–18.
[39]   Song H, Ahmad A, Farooq F, Ostrowski KA, Maślak M, Czarnecki S, et al. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr Build Mater 2021;308:1–15.
[40]   Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, et al. Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 2018;115:112–25.
[41]   Ashrafian A, Shokri F, Taheri Amiri MJ, Yaseen ZM, Rezaie-Balf M. Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model. Constr Build Mater 2020;230:117048.
[42]   Young BA, Hall A, Pilon L, Gupta P, Sant G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods. Cem Concr Res 2019;115:379–88.
[43]   Al-Musawi AA, Alwanas AAH, Salih SQ, Ali ZH, Tran MT, Yaseen ZM. Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model. Eng Comput 2020;36:1–11.
[44]   Yaseen ZM, Keshtegar B, Hwang H-J, Nehdi ML. Predicting reinforcing bar development length using polynomial chaos expansions. Eng Struct 2019;195:524–35.
[45]   Liu B, Wei B, Li H, Mao Y. Multipoint hybrid model for RCC arch dam displacement health monitoring considering construction interface and its seepage. Appl Math Model 2022;110:674–97.
[46]   Narendra BS, Sivapullaiah P V., Suresh S, Omkar SN. Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study. Comput Geotech 2006;33:196–208.
[47]   Ranjbar I, Toufigh V, Boroushaki M. A combination of deep learning and genetic algorithm for predicting the compressive strength of <scp>high‐performance</scp> concrete. Struct Concr 2022;23:2405–18.
[48]   Ly H-B, Nguyen T-A, Thi Mai H-V, Tran VQ. Development of deep neural network model to predict the compressive strength of rubber concrete. Constr Build Mater 2021;301:124081.
[50]   Pham BT, Ly H-B, Al-Ansari N, Ho LS. A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient. Sci Program 2021;2021:1–13.
[51]   Muhammad A, Külahcı F, Salh H, Hama Rashid PA. Long Short Term Memory networks (LSTM)-Monte-Carlo simulation of soil ionization using radon. J Atmos Solar-Terrestrial Phys 2021;221:105688.
[52]   Akan R, Keskin SN. The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils. SN Appl Sci 2019;1:843.
[53]   Algaifi HA, Alqarni AS, Alyousef R, Bakar SA, Ibrahim MHW, Shahidan S, et al. Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Eng J 2021;12:3629–39.
[54]   Wan Z, Xu Y, Šavija B. On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance. Materials (Basel) 2021;14:713.
[55]   Topçu İB, Sarıdemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 2008;41:305–11.
[56]   Prasad BKR, Eskandari H, Reddy BVV. Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Constr Build Mater 2009;23:117–28.
[57]   Ahmad A, Farooq F, Niewiadomski P, Ostrowski K, Akbar A, Aslam F, et al. Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials (Basel) 2021;14:1–21.
[58]   McCarthy MJ, Dhir RK. Development of high volume fly ash cements for use in concrete construction. Fuel 2005;84:1423–32.
[59]   Kayali MN, O. H. PROPERTIES OF HIGH-STRENGTH CONCRETE USING A FINE FLY ASH. Cem Concr Res 1998;28:1445–1452.
[60]   Siddique R, Khatib JM. Abrasion resistance and mechanical properties of high-volume fly ash concrete. Mater Struct Constr 2010;43:709–18.
[61]   Thomas MDA. Optimizing the Use of Fly Ash in Concrete. 2007.
[62]   Khambra G, Shukla P. Novel machine learning applications on fly ash based concrete: An overview. Mater Today Proc 2021.
[63]   Kavitha S, Varuna S, Ramya R. A comparative analysis on linear regression and support vector regression. Proc 2016 Online Int Conf Green Eng Technol IC-GET 2016 2017.
[64]   Khambra G, Shukla P. Novel machine learning applications on fly ash based concrete: An overview. Mater Today Proc 2021.
[65]   Su X, Yan X, Tsai CL. Linear regression. Wiley Interdiscip Rev Comput Stat 2012;4:275–94.
[66]   Charbuty B, Abdulazeez A. Classification Based on Decision Tree Algorithm for Machine Learning. J Appl Sci Technol Trends 2021;2:20–8.
[67]   Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. An introduction to decision tree modeling. J Chemom 2004;18:275–85.
[68]   Saxena R. How decision tree algorithm works. Dataaspirat 2017.
[69]   Pisner DA, Schnyer DM. Support vector machine. Elsevier Inc.; 2019.
[70]   Yu H, Kim S. SVM Tutorial: Classification, Regression, and Ranking. vol. 1–4. 2012.
[71]   Noble WS. What is a support vector machine? Nat Biotechnol 2006;24:1565–7.
[72]   Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., New York, NY, USA: ACM; 2016, p. 785–94.
[73]   Pan B. Application of XGBoost algorithm in hourly PM2.5 concentration prediction. IOP Conf Ser Earth Environ Sci 2018;113:1–7.
[74]   Biau G, Scornet E. A random forest guided tour. Test 2016;25:197–227.
[75]   Qi C, Huang B, Wu M, Wang K, Yang S, Li G. Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer. Materials (Basel) 2022;15.
[76]   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;2021.
[77]   Khan GM. Artificial neural network (ANNs). Stud Comput Intell 2018;725:39–55.
[78]   Agrawal SK. Understanding the Basics Of Artificial Neural Network,. Data Sci Blogathon, Anal Vidhya, 2021.
[79]   Ahmad A, Ostrowski KA, Maślak M, Farooq F, Mehmood I, Nafees A. Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature. Materials (Basel) 2021;14.
[80]   Gardner M., Dorling S. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 1998;32:2627–36.
[81]   Umeonyiagu I, Nwobi-Okoye C. Predicting Flexural Strength of Concretes Incorporating River Gravel Using Multi-Layer Perceptron Networks: A Case Study of Eastern Nigeria. Niger J Technol 2014;34:12.
[82]   SINGH B, SINGH B, SIHAG P, TOMAR A, SEHGAL A. Estimation of compressive strength of high-strength concrete by random forest and M5P model tree approaches. J Mater Eng Struct « JMES » 2019;6:583–92.
[83]   Jo H-S, Park C, Lee E, Choi HK, Park J. Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process. Sensors 2020;20:1927.
[84]   Bouzoubaa N, Zhang MH, Bilodeau A, Malhotra VM. Mechanical properties and durability of concrete made with high volume Fly ash blended cements. Cem Concr Compos 1998;SP178:575–603.
[85]   Nochaiya T, Wongkeo W, Chaipanich A. Utilization of fly ash with silica fume and properties of Portland cement-fly ash-silica fume concrete. Fuel 2010;89:768–74.
[86]   Atis CD. High-Volume Fly Ash Concrete with High Strength and Low Drying Shrinkage. J Mater Civ Eng 2003;15:153–6.
[87]   Kumar B, Tike GK, Nanda PK. Evaluation of Properties of High-Volume Fly-Ash Concrete. J Mater Civ Eng 2007:906–12.
[88]   Oner A, Akyuz S, Yildiz R. An experimental study on strength development of concrete containing fly ash and optimum usage of fly ash in concrete. Cem Concr Res 2005;35:1165–71.
[89]   Moffatt EG, Thomas MDA, Fahim A. Performance of high-volume fly ash concrete in marine environment. Cem Concr Res 2017;102:127–35.
[90]   Khatib JM. Performance of self-compacting concrete containing fly ash. Constr Build Mater 2008;22:1963–71.
[91]   Barbhuiya SA, Gbagbo JK, Russell MI, Basheer PAM. Properties of fly ash concrete modified with hydrated lime and silica fume. Constr Build Mater 2009;23:3233–9.
[92]   Reiner M, Rens K. High-Volume Fly Ash Concrete: Analysis and Application. Pract Period Struct Des Constr 2006;11:58–64.
[93]   Rivera F, Martínez P, Castro J, López M. Massive volume fly-ash concrete: A more sustainable material with fly ash replacing cement and aggregates. Cem Concr Compos 2015;63:104–12.
[94]   Li G. A new way to increase the long-term bond strength of new-to-old concrete by the use of fly ash. Cem Concr Res 2003;33:799–806.
[95]   Babu KG, Rao GSN. EFFICIENCY OF FLY ASH IN CONCRETE WITH AGE. Cem Concr Res 1996;26:465–76.
[96]   Cao C, Sun W, Qin H. The Analysis on strength and fly ash effect of roller-compacted concrete with high volume fly ash. Cem Concr Res 2000;30:71–5.
[97]   Ravina D, Mehta PK. COMPRESSIVE STRENGTH OF LOW CEMENT / HIGH FLY ASH CONCRETE. Cem Concr Res 1988;18:571–83.
[98]   Golewski GL. Effect of curing time on the fracture toughness of fly ash concrete composites. Compos Struct 2018;185:105–12.
[99]   Herath C, Gunasekara C, Law DW, Setunge S. Performance of high volume fly ash concrete incorporating additives: A systematic literature review. Constr Build Mater 2020;258:120606.
[100]  Montgomery DG. FLY ASH IN CONCRETE - A MICROSTEUCTURE STUDY. Cem Concr Res 1981;11:591–603.
[102]  Gopalan MK. Sorptivity of Fly Ash Concretes. Cem Concr Res 1996;26:1189–97.
[103]  Bouzoubaâ N, Lachemi M. Self-compacting concrete incorporating high volumes of class F fly ash. Cem Concr Res 2001;31:413–20.
[104]  Kayali O. Fly ash lightweight aggregates in high performance concrete. Constr Build Mater 2008;22:2393–9.
[105]  Kang S, Lloyd Z, Kim T, Ley MT. Predicting the compressive strength of fly ash concrete with the Particle Model. Cem Concr Res 2020;137:106218.
[106]  Rai P, Qiu W, Pei H, Chen J, Ai X, Liu Y, et al. Effect of Fly Ash and Cement on the Engineering Characteristic of Stabilized Subgrade Soil: An Experimental Study. Geofluids 2021;2021.
[107]  Venkateswara Rao A, Srinivasa Rao K. Effect of fly ash on strength of concrete. Circ Econ Fly Ash Manag 2019;14:125–34.
[108]  Malhotra VM. Durability of concrete incorporating high-volume of low-calcium (ASTM Class F) fly ash. Cem Concr Compos 1990;12:271–7.
[109]  Sun J, Shen X, Tan G, Tanner JE. Compressive strength and hydration characteristics of high-volume fly ash concrete prepared from fly ash. J Therm Anal Calorim 2019;136:565–80.
[110]  mike yi. A Complete Guide to Scatter Plots. Data Tutorials 2021.
[111]  Wang Y, Han F, Zhu L, Deussen O, Chen B. Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series. IEEE Trans Vis Comput Graph 2018;24:1141–54.
[112]  V. DD, Fischer RL, Fogelson DE. Prediction of compressive strength from other rock properties (Vol. 6702). US Dep. Inter. Bur. Mines., 1965.
[113]  Dauji S. Neural prediction of concrete compressive strength. Int J Mater Struct Integr 2018;12:17–35.
[114]  Chen N, Zhao S, Gao Z, Wang D, Liu P, Oeser M, et al. Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation. Constr Build Mater 2022;323:126580.
[115]  Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem Concr Res 2021;145:106449.
[116]  Marani A, Jamali A, Nehdi ML. Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks. Materials (Basel) 2020;13:4757.
[117]  Kumar Tipu R, Panchal VR, Pandya KS. An ensemble approach to improve BPNN model precision for predicting compressive strength of high-performance concrete. Structures 2022;45:500–8.
[118]  Gupta P, Gupta N, Saxena KK, Goyal S. Multilayer perceptron modelling of geopolymer composite incorporating fly ash and GGBS for prediction of compressive strength. Adv Mater Process Technol 2022;8:1441–55.
[119]  Ma Q, Li J, Aamer M, Huang G. Effect of Chinese milk vetch (astragalus sinicus l.) and rice straw incorporated in paddy soil on greenhouse gas emission and soil properties. Agronomy 2020;10.
[120]  Naser AH, Badr AH, Henedy SN, Ostrowski KA, Imran H. Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete. Case Stud Constr Mater 2022;17:e01262.
[121]  Akshita chugh. MAE, MSE, RMSE, Coefficient of Determination, Adjusted R Squared — Which Metric is Better? Anal Vidhya 2020.
[122]  Seif G. Understanding the 3 most common loss functions for Machine Learning Regression. Towar Data Sci 2019.
[123]  Zhang X, Akber MZ, Zheng W. Prediction of seven-day compressive strength of field concrete. Constr Build Mater 2021;305:124604.
[124]  Amin MN, Al-Hashem MN, Ahmad A, Khan K, Ahmad W, Qadir MG, et al. Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete. Materials (Basel) 2022;15:7800.
[125]  FERNANDO J. R-Squared. Investopedia 2021.
[126]  Kamalov F. Sensitivity Analysis for Feature Selection. Proc - 17th IEEE Int Conf Mach Learn Appl ICMLA 2018 2019:1466–70.
[127]  Arachchilage CB, Fan C, Zhao J, Huang G, Liu WV. A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill. J Rock Mech Geotech Eng 2023.
[128]  Taylor KE. Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 2001;106:7183–92.
[129]  Pandey M, Jamei M, Ahmadianfar I, Karbasi M, Lodhi AS, Chu X. Assessment of scouring around submerged spur dike in cohesive sediment mixtures: A comparative study on three rigorous machine learning models. J Hydrol 2021:127330.
[130]  Yesiloglu-Gultekin N, Gokceoglu C. A Comparison Among Some Non-linear Prediction Tools on Indirect Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Basalt. J Nondestruct Eval 2022;41:10.
[131]  Gupta S, Sihag P. Prediction of the compressive strength of concrete using various predictive modeling techniques. Neural Comput Appl 2022;34:6535–45.