Concrete strength and durability prediction through deep learning and artificial neural networks

Abstract

The mechanical and durability characteristics of concrete are crucial for designing and evaluating concrete structures throughout their entire operational lifespan. The main objective of this research is to use the deep learning (DL) method along with an artificial neural network (ANN) to predict the chloride migration coefficient and concrete compressive strength. An expansive experimental database of nearly 1100 data points was gathered from existing scientific literature. Four forecast models were created, utilizing between 10 and 12 input features. The ANN was used to address the missing data gaps in the literature. A comprehensive pre-processing approach was then implemented to identify outliers and encode data attributes. The use of mean absolute error (MAE) as an evaluation metric for regression tasks and the employment of a confusion matrix for classification tasks were found to produce accurate results. Additionally, both the compressive strength and chloride migration coefficient exhibit a high level of accuracy, above 0.85, in both regression and classification tasks. Moreover, a user-friendly web application was successfully developed in the present study using the Python programming language, improving the ability to integrate smoothly with the user’s device.

Graphical abstract

Keywords

chloride migration coefficient / compressive strength / concrete / artificial neural network / deep learning

References

[1] Feng D C, Liu Z T, Wang X D, Chen Y, Chang J Q, Wei D F, Jiang Z M. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Construction & Building Materials, 2020, 230: 117000

[2] Alqahtani F K, Zafar I. Characterization of processed lightweight aggregate and its effect on physical properties of concrete. Construction & Building Materials, 2020, 230: 116992

[3] Mansoori A, Mohtasham Moein M, Mohseni E. Effect of micro silica on fiber-reinforced self-compacting composites containing ceramic waste. Journal of Composite Materials, 2020, 55(1): 95–107

[4] Tahmouresi B, Nemati P, Asadi M A, Saradar A, Moein M M. Mechanical strength and microstructure of engineered cementitious composites: A new configuration for direct tensile strength, experimental and numerical analysis. Construction & Building Materials, 2021, 269: 121361

[5] Zain M F M, Abd S M. Multiple regression model for compressive strength prediction of high performance concrete. Journal of Applied Science, 2009, 9(1): 155–160

[6] Bharatkumar B H, Narayanan R, Raghuprasad B K, Ramachandramurthy D S. Mix proportioning of high performance concrete. Cement and Concrete Composites, 2001, 23: 71–80

[7] Bhanja S, Sengupta B. Investigations on the compressive strength of silica fume concrete using statistical methods. Cement and Concrete Research, 2002, 32(9): 1391–1394

[8] Oliver J, Huespe A E, Samaniego E, Chaves E W V. Continuum approach to the numerical simulation of material failure in concrete. International Journal for Numerical and Analytical Methods in Geomechanics, 2004, 28(7–8): 609–632

[9] Feng D C, Xie S C, Deng W N, Ding Z D. Probabilistic failure analysis of reinforced concrete beam–column sub-assemblage under column removal scenario. Engineering Failure Analysis, 2019, 100: 381–392

[10] Feng D C, Wang Z, Wu G. Progressive collapse performance analysis of precast reinforced concrete structures. Structural Design of Tall and Special Buildings, 2019, 28(5): e1588

[11] Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 2019, 61(4): 5–14

[12] Moein M M, Saradar A, Rahmati K, Mousavinejad S H G, Bristow J, Aramali V, Karakouzian M. Predictive models for concrete properties using machine learning and deep learning approaches: A review. Journal of Building Engineering, 2023, 63: 105444

[13] Liu Q, Sun P, Fu X, Zhang J, Yang H, Gao H, Li Y. Comparative analysis of BP neural network and RBF neural network in seismic performance evaluation of pier columns. Mechanical Systems and Signal Processing, 2020, 141: 106707

[14] Chou J S, Ngo N T, Pham A D. Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. Journal of Computing in Civil Engineering, 2016, 30(1): 08215001

[15] Behnood A, Verian K P, Gharehveran M M. Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength. Construction & Building Materials, 2015, 98: 519–529

[16] Han B, Wu Y, Liu L. Prediction and uncertainty quantification of compressive strength of high-strength concrete using optimized machine learning algorithms. Structural Concrete, 2022, 23(6): 3772–3785

[17] Abdeljaber O, Avci O, Inman D J. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks. Journal of Sound and Vibration, 2016, 363: 33–53

[18] Jiang X, Mahadevan S, Adeli H. Bayesian wavelet packet denoising for structural system identification. Structural Control & Health Monitoring, 2007, 14(2): 333–356

[19] HosseinzadehMDehestaniMHosseinzadehA. Exploring elastic properties of fly ash recycled aggregate concrete: Insights from multiscale modeling and machine learning. Structures, 59: 105720

[20] Huang J C, Ko K M, Shu M H, Hsu B M. Application and comparison of several machine learning algorithms and their integration models in regression problems. Neural Computing & Applications, 2020, 32(10): 5461–5469

[21] Hosseinzadeh M, Dehestani M, Hosseinzadeh A. Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms. Journal of Building Engineering, 2023, 76: 107006

[22] MüllerA CGuidoS. Introduction to Machine Learning with Python: A Guide for Data Scientists. Sebastopol: O’Reilly Media, 2016

[23] Rosental C. Certifying knowledge: The sociology of a logical theorem in artificial intelligence. American Sociological Review, 2003, 68(4): 623–644

[24] Langley P. The changing science of machine learning. Machine Learning, 2011, 82(3): 275–279

[25] Dietterich T G. Ensemble methods in machine learning. Lecture Notes in Computer Science, 2000, 1857: 1–15

[26] Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman D J. A review of vibration-based damage detection in civil structures: from traditional methods to Machine Learning and Deep Learning applications. Mechanical Systems and Signal Processing, 2021, 147: 107077

[27] Zhang C, Zhang X. Multi-target domain-based hierarchical dynamic instance segmentation method for steel defects detection. Neural Computing & Applications, 2023, 35(10): 7389–7406

[28] Schmidhuber J. Deep Learning in neural networks: An overview. Neural Networks, 2015, 61: 85–117

[29] Chu X H, Wang J Y, Li S X, Chai Y J, Guo Y Q. Empirical study on meta-feature characterization for multi-objective optimization problems. Neural Computing & Applications, 2022, 34(19): 16255–16273

[30] Deng F, He Y, Zhou S, Yu Y, Cheng H, Wu X. Compressive strength prediction of recycled concrete based on deep learning. Construction & Building Materials, 2018, 175: 562–569

[31] Kaloop M R, Gabr A R, El-Badawy S M, Arisha A, Shwally S, Hu J W. Predicting resilient modulus of recycled concrete and clay masonry blends for pavement applications using soft computing techniques. Frontiers of Structural and Civil Engineering, 2019, 13(6): 1379–1392

[32] Chou J S, Chiu C K, Farfoura M, Al-Taharwa I. Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. Journal of Computing in Civil Engineering, 2011, 25(3): 242–253

[33] Gholampour A, Mansouri I, Kisi O, Ozbakkaloglu T. Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Computing & Applications, 2020, 32(1): 295–308

[34] Deepa C, SathiyaKumari K, Pream S V. Prediction of the compressive strength of high performance concrete mix using tree based modeling. International Journal of Computer Applications, 2010, 6: 18–24

[35] Behnood A, Behnood V, Modiri Gharehveran M, Alyamac K E. Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Construction & Building Materials, 2017, 142: 199–207

[36] Ayaz Y, Kocamaz A F, Karakoç M B. Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers. Construction & Building Materials, 2015, 94: 235–240

[37] Dantas A T A, Batista Leite M, de Jesus Nagahama K. Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Construction & Building Materials, 2013, 38: 717–722

[38] Duan Z H, Kou S C, Poon C S. Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Construction & Building Materials, 2013, 44: 524–532

[39] Naderpour H, Poursaeidi O, Ahmadi M. Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks. Measurement, 2018, 126: 299–308

[40] Topçu I B, Saridemir M. Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Computational Materials Science, 2008, 42(1): 74–82

[41] Nikoo M, TorabianMoghadam F, Sadowski L. Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering, 2015, 2015: 849126

[42] Chithra S, Kumar S R R S, Chinnaraju K, Alfin Ashmita F. A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks. Construction & Building Materials, 2016, 114: 528–535

[43] Ranjbar I, Toufigh V, Boroushaki M. A combination of deep learning and genetic algorithm for predicting the compressive strength of high-performance concrete. Structural Concrete, 2022, 23(4): 2405–2418

[44] Kina C, Turk K, Tanyildizi H. Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models. Structural Concrete, 2022, 23(5): 3313–3330

[45] el Asri Y, Benaicha M, Zaher M, Hafidi Alaoui A. Prediction of the compressive strength of self-compacting concrete using artificial neural networks based on rheological parameters. Structural Concrete, 2022, 23(6): 3864–3876

[46] GuoHZhuangXRabczukT. 2021. A deep collocation method for the bending analysis of Kirchhoff plate. Computers Materials & Continua, 59(2): 433–456

[47] Guo H, Zhuang X, Chen P, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 38(6): 5173–5198

[48] Guo H, Zhuang X, Alajlan N, Rabczuk T. Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning. Computers & Mathematics with Applications, 2023, 143: 303–317

[49] Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

[50] Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A, Solids, 2021, 87: 104225

[51] Guo H, Zhuang X, Fu X, Zhu Y, Rabczuk T. Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials. Computational Mechanics, 2023, 72(3): 513–524

[52] Hoang N D, Chen C T, Liao K W. Prediction of chloride diffusion in cement mortar using multi-gene genetic programming and multivariate adaptive regression splines. Measurement, 2017, 112: 141–149

[53] Gao W, Chen X, Chen D. Genetic programming approach for predicting service life of tunnel structures subject to chloride-induced corrosion. Journal of Advanced Research, 2019, 20: 141–152

[54] Ahmad A, Farooq F, Ostrowski K A, Śliwa-Wieczorek K, Czarnecki S. Application of novel machine learning techniques for predicting the surface chloride concentration in concrete containing waste material. Materials, 2021, 14(9): 2297

[55] Liu K H, Zheng J K, Pacheco-Torgal F, Zhao X Y. Innovative modeling framework of chloride resistance of recycled aggregate concrete using ensemble-machine-learning methods. Construction & Building Materials, 2022, 337: 127613

[56] ASTM-C1202. Standard Test Method for Electrical Indication of Concrete’s Ability to Resist Chloride Ion Penetration. Philadelphia: American Society Testing and Materials, 2012

[57] Jin L, Dong T, Fan T, Duan J, Yu H, Jiao P, Zhang W. Prediction of the chloride diffusivity of recycled aggregate concrete using artificial neural network. Materials Today Communications, 2022, 32: 104137

[58] Alabdullah A A, Iqbal M, Zahid M, Khan K, Amin M N, Jalal F E. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Construction & Building Materials, 2022, 345: 128296

[59] Amin M N, Raheel M, Iqbal M, Khan K, Qadir M G, Jalal F E, Alabdullah A A, Ajwad A, Al-Faiad M A, Abu-Arab A M. Prediction of rapid chloride penetration resistance to assess the influence of affecting variables on metakaolin-based concrete using gene expression programming. Materials, 2022, 15(19): 6959

[60] YaoLRenLGongG. Evaluation of chloride diffusion in concrete using PSO-BP and BP neural network. In: Proceedings of IOP Conference Series (Earth and Environmental Science). Zhuhai: IOP Publishing, 2021, 687(1): 012037

[61] Cai R, Han T, Liao W, Huang J, Li D, Kumar A, Ma H. Prediction of surface chloride concentration of marine concrete using ensemble machine learning. Cement and Concrete Research, 2020, 136: 106164

[62] Tran A T, Le T H, Nguyen H M. Forecast of surface chloride concentration of concrete utilizing ensemble decision tree boosted. Journal of Science & Transport Technology, 2022, 2(1): 44–56

[63] Tran V Q. Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials. Construction & Building Materials, 2022, 328: 127103

[64] Guo Z, Guo R, Lin S. Multi-factor fuzzy prediction model of concrete surface chloride concentration with trained samples expanded by random forest algorithm. Marine Structures, 2022, 86: 103311

[65] Guo Z, Guo R, Yao G. Multi-factor model to predict surface chloride concentration of concrete based on fuzzy logic system. Case Studies in Construction Materials, 2022, 17: e01305

[66] Taffese W Z, Espinosa-Leal L. A machine learning method for predicting the chloride migration coefficient of concrete. Construction & Building Materials, 2022, 348: 128566

[67] Taffese W Z, Espinosa-Leal L. Prediction of chloride resistance level of concrete using machine learning for durability and service life assessment of building structures. Journal of Building Engineering, 2022, 60: 105146

[68] Tran V Q, Giap V L, Vu D P, George R C, Ho L S. Application of machine learning technique for predicting and evaluating chloride ingress in concrete. Frontiers of Structural and Civil Engineering, 2022, 16(9): 1153–1169

[69] Van RossumG. Python Programming Language. In: USENIX Annual Technical Conference. Santa Clara: CA, 2007, 1–36

[70] GilatA. MATLAB: An introduction with Applications. Hoboken: John Wiley & Sons, 2004

[71] Nordtest. NT Build 492. Concrete, mortar and cement-based repair materials: Chloride migration coefficient from non-steady-state migration experiments. Oslo: NORDTEST, 1999

[72] Costa A, Appleton J. Chloride penetration into concrete in marine environment—Part I: Main parameters affecting chloride penetration. Materials and Structures, 1999, 32(4): 252–259

[73] Thomas M D, Bamforth P B. Modelling chloride diffusion in concrete—Effect of fly ash and slag. Cement and Concrete Research, 1999, 29(4): 487–495

[74] Hou H B, Zhang G Z. Assessment on chloride contaminated resistance of concrete with non-steady-state migration method. Journal of Wuhan University of Technology—Materials Science Edition, 2004, 19(4): 6–8

[75] Hosseinzadeh M, Mousavi S S, Hosseinzadeh A, Dehestani M. An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset. Scientific Reports, 2023, 13(1): 15024

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