MLP neural network in google colaboratory to predict mechanical properties of manufactured-sand concrete
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Published:
Feb 28, 2023
Keywords:
Neural network, Python, Mechanical properties, Manufactured sand, Concrete
Section
Computer Science
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Abstract
The use of manufactured sand instead of natural sand in concrete can help preserve rivers or beaches where these natural sand is sourced from. The mechanical properties of these manufactured-sand concretes are comparable to those produced using natural sand. To predict and help optimize the mechanical properties of concrete made with manufactured sand, a model of ANN was developed using python 3.8 programming language in Google Colaboratory environtment. It was found that ANN model with LBFGS weight optimization algorithm and 50 hidden layer nodes has the best performance, with RMSE = 0.067081. The accuracy of prediction made with this model is calculated to be 90.58% by means of R-squared value and 95.37% by mean absolute error (MAE) value.
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How to Cite
Absa, M., Setiawan, T., Fatwa, I. and Hidayat, A. T. (2023) “MLP neural network in google colaboratory to predict mechanical properties of manufactured-sand concrete”, Jurnal Mantik, 6(4), pp. 3679-3687. doi: 10.35335/mantik.v6i4.3509.
References
Adhitya, R., Devi, I., & Sari, I. N. (2022). Prediction of Tidal Data Using Cloud Computing-Based Support Vector Regression in Alor , East Nusa Tenggara. Jurnal Mandiri IT, 11(1), 1–9.
Behnood, A., & Golafshani, E. M. (2018). Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202, 54–64. https://doi.org/10.1016/j.jclepro.2018.08.065
Bingöl, A. F., Tortum, A., & Gül, R. (2013). Neural networks analysis of compressive strength of lightweight concrete after high temperatures. Materials and Design, 52, 258–264. https://doi.org/10.1016/j.matdes.2013.05.022
Chopra, P., Sharma Professor, R. K., & Kumar Professor, M. (2014). Regression Models for the Prediction of Compressive Strength of Concrete with & without Fly ash. International Journal of Latest Trends in Engineering and Technology (IJLTET), 3(March 2014), 400.
Drebenstedt, C. B. X. L. C. (2021). Proceedings of the International Conference on Innovations for Sustainable and Responsible Mining. In International Conference of Innovations for Sustainable and Reponsible Mining (Vol. 109). https://link.springer.com/10.1007/978-3-030-60839-2
Firdaus. (2019). Dampak Lingkungan Dan Sosial Penggalian Pasir Sepanjang Aliran Sungai Di Kota Bima (Studi Di Kelurahan Rabadompu Timur Kota Bima). Jurnal Komunikasi dan Kebudayaan, 6(1), 9–26.
Harsiti, Muttaqin, Z., & Srihartini, E. (2022). Penerapan Metode Regresi Linier Sederhana Untuk Prediksi Persediaan Obat Jenis Tablet. JSiI (Jurnal Sistem Informasi), 9(1), 12–16. https://doi.org/10.30656/jsii.v9i1.4426
Hidayawanti, R., Yuhanah, Mayasari, D., & Wicaksono, B. (2020). The effect of m-sand and waste marble for strength of concrete. IOP Conference Series: Materials Science and Engineering, 930(1). https://doi.org/10.1088/1757-899X/930/1/012024
Ji, T., Chen, C. Y., Zhuang, Y. Z., & Chen, J. F. (2013). A mix proportion design method of manufactured sand concrete based on minimum paste theory. Construction and Building Materials, 44, 422–426. https://doi.org/10.1016/j.conbuildmat.2013.02.074
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15.
Ly, H. B., Pham, B. T., Van Dao, D., Le, V. M., Le, L. M., & Le, T. T. (2019). Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete. Applied Sciences (Switzerland), 9(18). https://doi.org/10.3390/app9183841
Mishra, M., Bhatia, A. S., & Maity, D. (2021). A comparative study of regression, neural network and neuro-fuzzy inference system for determining the compressive strength of brick–mortar masonry by fusing nondestructive testing data. Engineering with Computers, 37(1), 77–91. https://doi.org/10.1007/s00366-019-00810-4
Najafabadi, M. M., Khoshgoftaar, T. M., Villanustre, F., & Holt, J. (2017). Large-scale distributed L-BFGS. Journal of Big Data, 4(1). https://doi.org/10.1186/s40537-017-0084-5
Pradoto, R. G. K., Soemardi, B. W., Gazali, A., Putri, A. T., Purba, R. P., & Mahardika, I. (2022). The Technology Landscape of Construction Material in the Indonesian Construction Industry. IOP Conference Series: Earth and Environmental Science, 1022(1). https://doi.org/10.1088/1755-1315/1022/1/012017
Shen, W., Yang, Z., Cao, L., Cao, L., Liu, Y., Yang, H., Lu, Z., & Bai, J. (2016). Characterization of manufactured sand: Particle shape, surface texture and behavior in concrete. Construction and Building Materials, 114, 595–601. https://doi.org/10.1016/j.conbuildmat.2016.03.201
Tangguh, F., & Islami, Y. (2022). Analisis performa algoritma Stochastic Gradient Descent ( SGD ) dalam mengklasifikasi tahu berformalin. Indonesian Journal of Data and Science, 3(1), 1–8.
Van Dao, D., Ly, H. B., Trinh, S. H., Le, T. T., & Pham, B. T. (2019). Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials, 12(6). https://doi.org/10.3390/ma12060983
Wibawa, M. S. (2017). Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan. Jurnal Sistem dan Informatika (JSI), 11(1), 167–174. http://archive.ics.uci.edu/ml/datasets/Wine
Zhang, J., Li, D., & Wang, Y. (2020). Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models. Journal of Cleaner Production, 258, 120665. https://doi.org/10.1016/j.jclepro.2020.120665
Zhao, S., Hu, F., Ding, X., Zhao, M., Li, C., & Pei, S. (2017). Dataset of tensile strength development of concrete with manufactured sand. Data in Brief, 11(136), 469–472. https://doi.org/10.1016/j.dib.2017.02.043
Behnood, A., & Golafshani, E. M. (2018). Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202, 54–64. https://doi.org/10.1016/j.jclepro.2018.08.065
Bingöl, A. F., Tortum, A., & Gül, R. (2013). Neural networks analysis of compressive strength of lightweight concrete after high temperatures. Materials and Design, 52, 258–264. https://doi.org/10.1016/j.matdes.2013.05.022
Chopra, P., Sharma Professor, R. K., & Kumar Professor, M. (2014). Regression Models for the Prediction of Compressive Strength of Concrete with & without Fly ash. International Journal of Latest Trends in Engineering and Technology (IJLTET), 3(March 2014), 400.
Drebenstedt, C. B. X. L. C. (2021). Proceedings of the International Conference on Innovations for Sustainable and Responsible Mining. In International Conference of Innovations for Sustainable and Reponsible Mining (Vol. 109). https://link.springer.com/10.1007/978-3-030-60839-2
Firdaus. (2019). Dampak Lingkungan Dan Sosial Penggalian Pasir Sepanjang Aliran Sungai Di Kota Bima (Studi Di Kelurahan Rabadompu Timur Kota Bima). Jurnal Komunikasi dan Kebudayaan, 6(1), 9–26.
Harsiti, Muttaqin, Z., & Srihartini, E. (2022). Penerapan Metode Regresi Linier Sederhana Untuk Prediksi Persediaan Obat Jenis Tablet. JSiI (Jurnal Sistem Informasi), 9(1), 12–16. https://doi.org/10.30656/jsii.v9i1.4426
Hidayawanti, R., Yuhanah, Mayasari, D., & Wicaksono, B. (2020). The effect of m-sand and waste marble for strength of concrete. IOP Conference Series: Materials Science and Engineering, 930(1). https://doi.org/10.1088/1757-899X/930/1/012024
Ji, T., Chen, C. Y., Zhuang, Y. Z., & Chen, J. F. (2013). A mix proportion design method of manufactured sand concrete based on minimum paste theory. Construction and Building Materials, 44, 422–426. https://doi.org/10.1016/j.conbuildmat.2013.02.074
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15.
Ly, H. B., Pham, B. T., Van Dao, D., Le, V. M., Le, L. M., & Le, T. T. (2019). Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete. Applied Sciences (Switzerland), 9(18). https://doi.org/10.3390/app9183841
Mishra, M., Bhatia, A. S., & Maity, D. (2021). A comparative study of regression, neural network and neuro-fuzzy inference system for determining the compressive strength of brick–mortar masonry by fusing nondestructive testing data. Engineering with Computers, 37(1), 77–91. https://doi.org/10.1007/s00366-019-00810-4
Najafabadi, M. M., Khoshgoftaar, T. M., Villanustre, F., & Holt, J. (2017). Large-scale distributed L-BFGS. Journal of Big Data, 4(1). https://doi.org/10.1186/s40537-017-0084-5
Pradoto, R. G. K., Soemardi, B. W., Gazali, A., Putri, A. T., Purba, R. P., & Mahardika, I. (2022). The Technology Landscape of Construction Material in the Indonesian Construction Industry. IOP Conference Series: Earth and Environmental Science, 1022(1). https://doi.org/10.1088/1755-1315/1022/1/012017
Shen, W., Yang, Z., Cao, L., Cao, L., Liu, Y., Yang, H., Lu, Z., & Bai, J. (2016). Characterization of manufactured sand: Particle shape, surface texture and behavior in concrete. Construction and Building Materials, 114, 595–601. https://doi.org/10.1016/j.conbuildmat.2016.03.201
Tangguh, F., & Islami, Y. (2022). Analisis performa algoritma Stochastic Gradient Descent ( SGD ) dalam mengklasifikasi tahu berformalin. Indonesian Journal of Data and Science, 3(1), 1–8.
Van Dao, D., Ly, H. B., Trinh, S. H., Le, T. T., & Pham, B. T. (2019). Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials, 12(6). https://doi.org/10.3390/ma12060983
Wibawa, M. S. (2017). Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan. Jurnal Sistem dan Informatika (JSI), 11(1), 167–174. http://archive.ics.uci.edu/ml/datasets/Wine
Zhang, J., Li, D., & Wang, Y. (2020). Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models. Journal of Cleaner Production, 258, 120665. https://doi.org/10.1016/j.jclepro.2020.120665
Zhao, S., Hu, F., Ding, X., Zhao, M., Li, C., & Pei, S. (2017). Dataset of tensile strength development of concrete with manufactured sand. Data in Brief, 11(136), 469–472. https://doi.org/10.1016/j.dib.2017.02.043
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