PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK
Monika KULISZ
m.kulisz@pollub.plLublin University of Technology, Faculty of Management, Department of Organisation of Enterprise (Poland)
Justyna KUJAWSKA
Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels (Poland)
Zulfiya AUBAKIROVA
D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction (Kazakhstan)
Gulnaz ZHAIRBAEVA
D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, (Kazakhstan)
Tomasz WAROWNY
Lublin University of Technology, Faculty of Management, Department of Quantitative Methods in Management (Poland)
Abstract
The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents 0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.
Keywords:
ANN, the compressive strength, RCA, MLP, RBFReferences
Anysz, H., & Narloch, P. (2019). Designing the composition of cement stabilized rammed earth using artificial neural networks. Materials, 12(9), 1396. https://doi.org/10.3390/ma12091396
DOI: https://doi.org/10.3390/ma12091396
Google Scholar
Atici, U. (2011). Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems with Applications, 38(8), 9609–9618. https://doi.org/10.1016/j.eswa.2011.01.156
DOI: https://doi.org/10.1016/j.eswa.2011.01.156
Google Scholar
British Standards Institution. (1988). British DOE (Department of Environment) Method (1988) Design of Normal Concrete Mixes. British Standards Institution. (2009). BS EN 12390-3:2009 Testing hardened concrete. Compressive strength of
Google Scholar
test specimens. https://bsol.bsigroup.com/en/Bsol-Item-Detail-Page/?pid=000000000030164906
Google Scholar
CEM II/B-V 32,5 R. (n.d.). Lafarge. Retrieved June 10, 2022 from https://www.lafarge.pl/cement-cem-iib-v325-r-disabled-page
Google Scholar
Chen, H., Qian, C., Liang, C., & Kang, W. (2018). An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack. PLoS ONE, 13(1), 1–17. https://doi.org/10.1371/journal.pone.0191370
DOI: https://doi.org/10.1371/journal.pone.0191370
Google Scholar
Chopra, P., Kumar, R., & Kumar, M. (2015). Artificial Neural Networks for the Prediction of Compressive Strength of Concrete. International Journal of Applied Science and Engineering, 13, 187–204.
Google Scholar
Chou, J., Chiu, C., Farofura, M., & Al-Tharawa, I. (2011). Optimizing the Prediction Accuracy of Concrete compressive Strength Based on a Comparison of Data-Mining Techniques. Journal of Computing in Civil Engineering, 25(3), 1171.
DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000088
Google Scholar
Dahmoune, F., Remini, H., Dairi, S., Aoun, O., Moussi, K., Bouaoudia-Madi, N., Adjeroud, N., Kadri, N., Lefsih, K., Boughani, L., Mouni, L., Nayak, B., & Madani, K. (2015). Ultrasound assisted extraction of phenolic compounds from P. lentiscus L. leaves: Comparative study of artificial neural network (ANN) versus degree of experiment for prediction ability of phenolic compounds recovery. Industrial Crops and Products, 77(0926–6690), 251–261. https://doi.org/https://doi.org/10.1016/j.indcrop.2015.08.062
DOI: https://doi.org/10.1016/j.indcrop.2015.08.062
Google Scholar
Dantas, A. T. A., Leite, M. B., & Nagahama, K. J. (2013). Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Construction and Building Materials, 38, 717–722. https://doi.org/10.1016/j.conbuildmat.2012.09.026
DOI: https://doi.org/10.1016/j.conbuildmat.2012.09.026
Google Scholar
Das, S., Swetapadma, A., & Panigrahi, C. (2019). A study on the application of artificial intelligence techniques for predicting the heating and cooling loads of buildings. Journal of Green Building, 14(3), 115–128. https://doi.org/10.3992/1943-4618.14.3.115
DOI: https://doi.org/10.3992/1943-4618.14.3.115
Google Scholar
Deepa, C., Kumari, K. S., & Sudha, V. P. (2010). Prediction of the compressive strength of high performance concrete mix using tree based modeling. International Journal of Computer Applications, 6(5), 0975–8887.
DOI: https://doi.org/10.5120/1076-1406
Google Scholar
Erdal, H. I., Karakurt, O., & Namli, E. (2013). High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence, 26(4), 1246–1254. https://doi.org/10.1016/j.engappai.2012.10.014
DOI: https://doi.org/10.1016/j.engappai.2012.10.014
Google Scholar
Fei, X., Youfu, S., & Xuejun, R. (2019). A simulation analysis method based on PSO-RBF model and its application. Cluster Computing, 22(s1), 2255–2261. https://doi.org/10.1007/s10586-018-2596-y
DOI: https://doi.org/10.1007/s10586-018-2596-y
Google Scholar
Gjorv, O. D., & Sakai, K. (2014). Concrete Technology for a Sustainable Development in the 21st Century. CRC Press. https://doi.org/https://doi.org/10.1201/9781482272215
DOI: https://doi.org/10.1201/9781482272215
Google Scholar
Hammoudi, A., Moussaceb, K., Belebchouche, C., & Dahmoune, F. (2019). Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Construction and Building Materials, 209, 425–436. https://doi.org/10.1016/j.conbuildmat.2019.03.119
DOI: https://doi.org/10.1016/j.conbuildmat.2019.03.119
Google Scholar
Han, Y., Fu, S., Wang, S., & Xie, Z. (2018). Study on Adiabatic Temperature Rise Reflecting Hydration Degree of Concrete. Advances in Materials Science and Engineering, 2018, 435049. https://doi.org/10.1155/2018/1435049
DOI: https://doi.org/10.1155/2018/1435049
Google Scholar
Karpiński, R. (2022). Knee joint osteoarthritis diagnosis based on selected acoustic signal discriminants using machine learning. Applied Computer Science, 18(2), 71–85. https://doi.org/10.35784/acs-2022-14
DOI: https://doi.org/10.35784/acs-2022-14
Google Scholar
Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022a). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint. Sensors, 22(10), 3765. http://dx.doi.org/10.3390/s22103765
DOI: https://doi.org/10.3390/s22103765
Google Scholar
Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022b). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint. Sensors, 22(6), 2176. http://dx.doi.org/10.3390/s22062176
DOI: https://doi.org/10.3390/s22062176
Google Scholar
Kurpinska, M., & Kułak, L. (2019). Predicting performance of lightweight concrete with granulated expanded Glass and Ash aggregate by means of using Artificial Neural Networks. Materials, 12(12), 2002. https://doi.org/10.3390/ma12122002
DOI: https://doi.org/10.3390/ma12122002
Google Scholar
Machrowska, A., Karpiński, R., Jonak, J., Szabelski, J., & Krakowski, P. (2020a). Numerical prediction of the component-ratio-dependent compressive strength of bone cement. Applied Computer Science, 16(3), 88-101. https://doi.org/10.23743/acs-2020-24
DOI: https://doi.org/10.35784/acs-2020-24
Google Scholar
Machrowska, A., Szabelski, J., Karpiński, R., Krakowski, P., Jonak, J., & Jonak, K. (2020b). Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements. Materials, 13(23), 5419. http://dx.doi.org/10.3390/ma13235419
DOI: https://doi.org/10.3390/ma13235419
Google Scholar
Naderpour, H., Rafiean, A. H., & Fakharian, P. (2018). Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16(January), 213–219. https://doi.org/10.1016/j.jobe.2018.01.007
DOI: https://doi.org/10.1016/j.jobe.2018.01.007
Google Scholar
Orosa, J. A., Vergara, D., Costa, Á. M., & Bouzón, R. (2019). A novel method based on neural networks for designing internal coverings in buildings: Energy saving and thermal comfort. Applied Sciences, 9(10), 2140. https://doi.org/10.3390/app9102140
DOI: https://doi.org/10.3390/app9102140
Google Scholar
Parichatprecha, R., & Nimityongskul, P. (2009). Analysis of durability of high performance concrete using artificial neural networks. Construction and Building Materials, 23(2), 910–917. https://doi.org/10.1016/j.conbuildmat.2008.04.015
DOI: https://doi.org/10.1016/j.conbuildmat.2008.04.015
Google Scholar
Pezeshki, Z., & Mazinani, S. M. (2019). Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artificial Intelligence Review, 52(1), 495–525. https://doi.org/10.1007/s10462-018-9630-6
DOI: https://doi.org/10.1007/s10462-018-9630-6
Google Scholar
Pytka, J., Budzyński, P., Tomiło, P., Michałowska, J., Błażejczak, D., Gnapowski, E., Pytka, J., & Gierczak, K. (2022). Measurement of aircraft ground roll distance during takeoff and landing on a grass runway. Measurement, 195, 111130. https://doi.org/10.1016/J.MEASUREMENT.2022.111130
DOI: https://doi.org/10.1016/j.measurement.2022.111130
Google Scholar
Qmran, B. A., Chen, Q., Asce, A. M., & Jin, R. (2016). Comparison of Different Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete. Journal of Computing in Civil Engineering, 30(6), 2208.
DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000596
Google Scholar
Rymarczyk, T., Kłosowski, G., Hoła, A., Sikora, J., Wołowiec, T., Tchórzewski, P., & Skowron, S. (2021). Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls. Energies, 14(10), 2777. http://dx.doi.org/10.3390/en14102777
DOI: https://doi.org/10.3390/en14102777
Google Scholar
Rymarczyk, T., Klosowski, G., Kozlowski, E., & Tchórzewski, P. (2019). Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography. Sensors, 19(7), 1521. https://doi.org/10.3390/S19071521
DOI: https://doi.org/10.3390/s19071521
Google Scholar
Szabelski, J., Karpiński, R., & Machrowska, A. (2022). Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios. Materials, 15(3), 721. http://dx.doi.org/10.3390/ma15030721
DOI: https://doi.org/10.3390/ma15030721
Google Scholar
Szala, M., Awtoniuk, M., Latka, L., MacEk, W., & Branco, R. (2021). Artificial neural network model of hardness, porosity and cavitation erosion wear of APS deposited Al2O3 -13 wt% TiO2 coatings. Journal of Physics: Conference Series, 1736(1), 012033. https://doi.org/10.1088/1742-6596/1736/1/012033
DOI: https://doi.org/10.1088/1742-6596/1736/1/012033
Google Scholar
Topçu, I. B., & Saridemir, M. (2008). Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Computational Materials Science, 42(1), 74–82. https://doi.org/10.1016/j.commatsci.2007.06.011
DOI: https://doi.org/10.1016/j.commatsci.2007.06.011
Google Scholar
Yeh, I. C., & Lien, L. C. (2009). Knowledge discovery of concrete material using Genetic Operation Trees. Expert Systems with Applications, 36(3 PART 2), 5807–5812. https://doi.org/10.1016/j.eswa.2008.07.004
DOI: https://doi.org/10.1016/j.eswa.2008.07.004
Google Scholar
Authors
Monika KULISZm.kulisz@pollub.pl
Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprise Poland
Authors
Justyna KUJAWSKALublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels Poland
Authors
Zulfiya AUBAKIROVAD. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction Kazakhstan
Authors
Gulnaz ZHAIRBAEVAD. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, Kazakhstan
Authors
Tomasz WAROWNYLublin University of Technology, Faculty of Management, Department of Quantitative Methods in Management Poland
Statistics
Abstract views: 176PDF downloads: 154
License
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Jerzy JÓZWIK, Magdalena ZAWADA-MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ, Kamil CYBUL, MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Monika KULISZ, EVALUATION OF SAP SYSTEM IMPLEMENTATION IN AN ENTERPRISE OF THE AUTOMOTIVE INDUSTRY – CASE STUDY , Applied Computer Science: Vol. 14 No. 4 (2018)
Similar Articles
- Robert KARPIŃSKI, Przemysław KRAKOWSKI, Józef JONAK, Anna MACHROWSKA, Marcin MACIEJEWSKI, COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES , Applied Computer Science: Vol. 19 No. 4 (2023)
- Mateusz Sawa, Mirosław Szala, Weronika Henzler, INNOVATIVE DEVICE FOR TENSILE STRENGTH TESTING OF WELDED JOINTS: 3D MODELLING, FEM SIMULATION AND EXPERIMENTAL VALIDATION OF TEST RIG – A CASE STUDY , Applied Computer Science: Vol. 17 No. 3 (2021)
- Anna MACHROWSKA, Robert KARPIŃSKI, Józef JONAK, Jakub SZABELSKI, NUMERICAL PREDICTION OF THE COMPONENT-RATIO-DEPENDENT COMPRESSIVE STRENGTH OF BONE CEMENT , Applied Computer Science: Vol. 16 No. 3 (2020)
- Rafał KLIZA, Karol ŚCISŁOWSKI, Ksenia SIADKOWSKA, Jacek PADYJASEK, Mirosław WENDEKER, STRENGTH ANALYSIS OF A PROTOTYPE COMPOSITE HELICOPTER ROTOR BLADE SPAR , Applied Computer Science: Vol. 18 No. 1 (2022)
- Robert KARPIŃSKI, KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Grzegorz KŁOSOWSKI, Tomasz KLEPKA, Agnieszka NOWACKA, NEURAL CONTROLLER FOR THE SELECTION OF RECYCLED COMPONENTS IN POLYMER-GYPSY MORTARS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Paweł BAŁON, Edward REJMAN, Bartłomiej KIEŁBASA, Janusz SZOSTAK, Robert SMUSZ, NUMERICAL AND EXPERIMENTAL ANALYSIS OF THE STRENGTH OF TANKS DEDICATED TO HOT UTILITY WATER , Applied Computer Science: Vol. 14 No. 4 (2018)
- Saheed A. ADEWUYI, Segun AINA, Adeniran I. OLUWARANTI, A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA , Applied Computer Science: Vol. 16 No. 1 (2020)
- Anna MACHROWSKA, Robert KARPIŃSKI, Marcin MACIEJEWSKI, Józef JONAK, Przemysław KRAKOWSKI, APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY , Applied Computer Science: Vol. 20 No. 2 (2024)
You may also start an advanced similarity search for this article.
