Development of innovative intelligent model to estimate the strength properties of hemp bio composite mixture using the combination of water cycle algorithm and MARS method

Document Type : Research Article

Authors

1 Department of civil engineering, Roudehen Branch, Islamic Azad University, Tehran, Iran

2 Islamic, Azad University, Roudhen, branch, Roudhen, Iran

3 Department of civil engineering, islamic azad university, roudehen branch, roudehen, iran

Abstract

Considering the characteristics of ordinary concrete, which is a composite material with characteristics such as resistance and low tensile strain range. Basically, the two main weaknesses of concrete, which include the brittle behavior of concrete and the weakness in its elasticity, have made the use of concrete structures made of ordinary concrete face major problems. In general, by adding fibers to the concrete mixture, it is possible to improve the mechanical properties of concrete. In this research, an innovative approach using the water cycle algorithm was used to combine with the adaptive multivariate regression spline method (MARS) to model and predict the compressive strength and tensile strength of concrete containing hemp biocomposites. For the development of each of the proposed models, 153 mixing designs along with their compressive strength results were collected from authoritative articles. After analyzing and evaluating the influencing parameters by the Mallow coefficient, the input parameters to the smart models include the ratio of water to cement base materials, the ratio of hemp seeds to cement base materials, the weight percentage of hemp seeds, cement base materials, seeds hemp, density of cement base material, density of dry material and strength of cement base material were selected. The results showed that the correlation coefficient for the compressive strength model for MARS optimized with the algorithm and Mars is 0.991 and 0.971, respectively, and the tensile strength is 0.928 and 0.911, respectively, in the training and testing phase. Investigations show that the proposed MARS model optimized with a meta-heuristic algorithm has good performance and high accuracy in estimating the compressive strength and tensile strength of concrete containing hemp biocomposite. The external validation results also show that the proposed approach can be introduced as predictive models and the correlation between predicted values ​​and experimental values ​​cannot be random.

Keywords

Main Subjects

References

[1] M. Gioffré, A. Vincenzini, N. Cavalagli, V. Gusella, M.A. Caponero, A. Terenzi, C. Pepi, A novel hemp-fiber bio-composite material for strengthening of arched structures: Experimental investigation, Construction and Building Materials, 308 (2021) 124969.
[2] S. Benfratello, C. Capitano, G. Peri, G. Rizzo, G. Scaccianoce, G. Sorrentino, Thermal and structural properties of a hemp–lime biocomposite, Construction and Building Materials, 48 (2013) 745-754.
[3] S. Barbhuiya, B. Bhusan Das, A comprehensive review on the use of hemp in concrete, Construction and Building Materials, 341 (2022) 127857.
[4] Y. Florentin, D. Pearlmutter, B. Givoni, E. Gal, A life-cycle energy and carbon analysis of hemp-lime bio-composite building materials, Energy and Buildings, 156 (2017) 293-305.
[5] N. Stevulova, J. Cigasova, I. Schwarzova, A. Sicakova, J. Junak, Sustainable Bio-Aggregate-Based Composites Containing Hemp Hurds and Alternative Binder, in:  Buildings, 2018.
[6] M.R. Ahmad, B. Chen, S. Yousefi Oderji, M. Mohsan, Development of a new bio-composite for building insulation and structural purpose using corn stalk and magnesium phosphate cement, Energy and Buildings, 173 (2018) 719-733.
[7] J. Zorica, M. Sinka, G. Sahmenko, L. Vitola, A. Korjakins, D. Bajare, Hemp Biocomposite Boards Using Improved Magnesium Oxychloride Cement, in:  Energies, 2022.
[8] C. Narattha, S. Wattanasiriwech, D. Wattanasiriwech, Effect of magnesium sulfate on properties of low calcium fly ash based-geopolymer- treated hemp shiv bio-concrete, Construction and Building Materials, 392 (2023) 131714.
[9] A. Ashrafian, M.J.T. Amiri, F. Haghighi, Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques, in, 2019.
[10] M.J. Taheri Amiri, A. Ashrafian, F.R. Haghighi, M. Javaheri Barforooshi, Prediction of the Compressive Strength of Self-compacting Concrete containing Rice Husk Ash using Data Driven Models, mdrsjrns, 19(1) (2019) 209-221.
[11] A. Saberi, H. Ahmadi, D.S. Shayegan, A. Amirkardoust, A study on the impact of water storage and regulation projects on sustainable development goals using mouth brooding fish (MBF) algorithm, Sustainable Water Resources Management, 9(6) (2023) 196.
[12] D. Sedaghat Shayegan, A. Lork, S.A.H. Hashemi, Mouth brooding fish algorithm for cost optimization of reinforced concrete one way ribbed slabs, IUST, 9(3) (2019) 411-422.
[13] D. Sedaghat Shayegan, A. Lork, S.A.H. Hashemi, A New Hybrid Algorithm for Cost Optimization of Waffle Slab, Slovak Journal of Civil Engineering, 28(3) (2020) 40-46.
[14] D. Sedaghat Shayegan, A. Lork, S.A.H. Hashemi, Optimum cost design of reinforced concrete slabs using a metaheuristic algorithm., International Journal of Optimization in Civil Engineering, 12(4) (2022) 545-555.
[15] J. Duan, P.G. Asteris, H. Nguyen, X.-N. Bui, H. Moayedi, A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model, Eng. with Comput., 37(4) (2021) 3329–3346.
[16] K. Nasrollahzadeh, M.M. Basiri, Prediction of shear strength of FRP reinforced concrete beams using fuzzy inference system, Expert Syst. Appl., 41 (2014) 1006-1020.
[17] A.-D. Pham, N.-D. Hoang, Q.-T. Nguyen, Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression, Journal of Computing in Civil Engineering, 30(3) (2016) 06015002.
[18] M. Açikgenç, M. Ulaş, K.E. Alyamaç, Using an Artificial Neural Network to Predict Mix Compositions of Steel Fiber-Reinforced Concrete, Arabian Journal for Science and Engineering, 40(2) (2015) 407-419.
[19] B. Kiani, A.H. Gandomi, S. Sajedi, R.Y. Liang, New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach, Journal of Materials in Civil Engineering, 28(10) (2016) 04016092.
[20] T. Kalman Šipoš, I. Miličević, R. Siddique, Model for mix design of brick aggregate concrete based on neural network modelling, Construction and Building Materials, 148 (2017) 757-769.
[21] I. Kara, Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming, Neural Computing and Applications, 23 (2012).
[22] A. Behnood, V. Behnood, M. Modiri Gharehveran, K.E. Alyamac, Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm, Construction and Building Materials, 142 (2017) 199-207.
[23] Z.M. Yaseen, M.T. Tran, S. Kim, T. Bakhshpoori, R.C. Deo, Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach, Engineering Structures, 177 (2018) 244-255.
[24] S. Khan, M. Ali Khan, A. Zafar, M.F. Javed, F. Aslam, M.A. Musarat, N.I. Vatin, Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence, Materials (Basel), 15(1) (2021).
[25] L. Sun, M. Koopialipoor, D.J. Armaghani, R. Tarinejad, M.M. Tahir, Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples, Eng. with Comput., 37(2) (2021) 1133–1145.
[26] Ł. Sadowski, M. Piechówka-Mielnik, T. Widziszowski, A. Gardynik, S. Mackiewicz, Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust, Journal of Cleaner Production, 212 (2019) 727-740.
[27] P. Asteris, A. Ashrafian, M. Rezaie-Balf, Prediction of the Compressive Strength of Self-Compacting Concrete using Surrogate Models, Computers and Concrete, 24 (2019) 137-150.
[28] M.A. DeRousseau, E. Laftchiev, J.R. Kasprzyk, B. Rajagopalan, W.V. Srubar, A comparison of machine learning methods for predicting the compressive strength of field-placed concrete, Construction and Building Materials, 228 (2019) 116661.
[29] E.M. Golafshani, A. Behnood, M. Arashpour, Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer, Construction and Building Materials, 232 (2020) 117266.
[30] Z.H. Duan, S.C. Kou, C.S. Poon, Prediction of compressive strength of recycled aggregate concrete using artificial neural networks, Construction and Building Materials, 40 (2013) 1200-1206.
[31] M. Shahnewaz, M.S. Alam, Genetic algorithm for predicting shear strength of steel fiber reinforced concrete beam with parameter identification and sensitivity analysis, Journal of Building Engineering, 29 (2020) 101205.
[32] A.A. Shahmansouri, M. Yazdani, S. Ghanbari, H. Akbarzadeh Bengar, A. Jafari, H. Farrokh Ghatte, Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite, Journal of Cleaner Production, 279 (2021) 123697.
[33] U.K. Sevim, H.H. Bilgic, O.F. Cansiz, M. Ozturk, C.D. Atis, Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques, Construction and Building Materials, 271 (2021) 121584.
[34] J. Rahman, K.S. Ahmed, N.I. Khan, K. Islam, S. Mangalathu, Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach, Engineering Structures, 233 (2021) 111743.
[35] H. Sabetifar, M. Nematzadeh, An evolutionary approach for formulation of ultimate shear strength of steel fiber-reinforced concrete beams using gene expression programming, Structures, 34 (2021) 4965-4976.
[36] A. Ashrafian, E. Panahi, S. Salehi, M.J. Taheri Amiri, On the implementation of the interpretable data-intelligence model for designing service life of structural concrete in a marine environment, Ocean Engineering, 256 (2022) 111523.
[37] A. Ashrafian, E. Panahi, S. Salehi, M. Karoglou, P.G. Asteris, Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques, Structures, 48 (2023) 1209-1229.
[38] J.H. Friedman, Multivariate Adaptive Regression Splines, The Annals of Statistics, 19(1) (1991) 1-67.
[39] M. Nasir, A. Sadollah, Y.H. Choi, J.H. Kim, A comprehensive review on water cycle algorithm and its applications, Neural Computing and Applications, 32(23) (2020) 17433-17488.
[40] H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi, Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers & Structures, 110-111 (2012) 151-166.
[41] L. Arnaud, E. Gourlay, Experimental study of parameters influencing mechanical properties of hemp concretes, Construction and Building Materials, 28(1) (2012) 50-56.
[42] R. del Valle-Zermeño, J.E. Aubert, A. Laborel-Préneron, J. Formosa, J.M. Chimenos, Preliminary study of the mechanical and hygrothermal properties of hemp-magnesium phosphate cements, Construction and Building Materials, 105 (2016) 62-68.
[43] M.R. Ahmad, B. Chen, M.A. Haque, S.F. Ali Shah, Development of a sustainable and innovant hygrothermal bio-composite featuring the enhanced mechanical properties, Journal of Cleaner Production, 229 (2019) 128-143.
[44] M.R. Ahmad, B. Chen, Influence of type of binder and size of plant aggregate on the hygrothermal properties of bio-concrete, Construction and Building Materials, 251 (2020) 118981.
[45] M.R. Ahmad, B. Chen, M.A. Haque, S.F.A. Shah, Utilization of industrial and hazardous waste materials to formulate energy-efficient hygrothermal bio-composites, Journal of Cleaner Production, 250 (2020) 119469.
[46] A. Ashrafian, A.A. Shahmansouri, H. Akbarzadeh Bengar, A. Behnood, Post-fire behavior evaluation of concrete mixtures containing natural zeolite using a novel metaheuristic-based machine learning method, Archives of Civil and Mechanical Engineering, 22 (2022) 101.
[47] W. Zhang, A.T.C. Goh, Multivariate adaptive regression splines and neural network models for prediction of pile drivability, Geoscience Frontiers, 7(1) (2016) 45-52.
[48] C. Niyigena, S. Amziane, A. Chateauneuf, L. Arnaud, L. Bessette, F. Collet, C. Lanos, G. Escadeillas, M. Lawrence, C. Magniont, S. Marceau, S. Pavia, U. Peter, V. Picandet, M. Sonebi, P. Walker, Variability of the mechanical properties of hemp concrete, Materials Today Communications, 7 (2016) 122-133.
Amirkabir Journal of Civil Engineering
Volume 56, Issue 12
2025
Pages 1557-1582

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