Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength
Source abstract
Abstract
In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Tunneling and Rock Mechanics · Drilling and Well Engineering · Rock Mechanics and Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
UTM Construction Research Centre, Institute for Smart, Infrastructure and Innovative Construction (ISIIC), Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
References (100+)
Show source-provided references
1. Prediction of compressive and tensile strength of limestone via genetic programming10.1016/j.eswa.2007.06.006 ↗
2. Predicting the compressive and tensile strength of rocks from indentation hardness index
3. Predicting the Uniaxial Compressive and Tensile Strengths of Gypsum Rock by Point Load Testing10.1007/s00603-011-0196-8 ↗
4. Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks10.1016/S1365-1609(00)00078-2 ↗
5. Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples
6. Ulusay, R., Hudson, J.A., and ISRM (2007). The Complete ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 1974–2006, ISRM Turkish Natl Group. Commission on Testing Methods, International Society for Rock Mechanics.
7. Dominant rock properties affecting the penetration rate of percussive drills10.1016/S1365-1609(03)00063-7 ↗
8. Use of the block punch test to predict the compressive and tensile strengths of rocks10.1016/j.ijrmms.2012.01.016 ↗
9. Sheorey, P.R. (1997). Empirical Rock Failure Criteria, A A Balkema.
10. A Review of the Tensile Strength of Rock: Concepts and Testing10.1007/s10706-014-9732-0 ↗
11. Prediction of the uniaxial compressive strength of sandstone using various modeling techniques10.1016/j.ijrmms.2016.03.018 ↗
12. Koopialipoor, M., Fahimifar, A., Ghaleini, E.N., Momenzadeh, M., and Armaghani, D.J. (2019). Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng. Comput., 1–13.10.1007/s00366-019-00701-8 ↗
13. Zhou, J., Aghili, N., Ghaleini, E.N., Bui, D.T., Tahir, M.M., and Koopialipoor, M. (2019). A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Eng. Comput., 1–11.10.1007/s00366-019-00726-z ↗
14. Krill herd algorithm-based neural network in structural seismic reliability evaluation10.1080/15376494.2018.1430874 ↗
15. Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors
16. Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams10.12989/sem.2013.46.6.853 ↗
17. Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam10.1007/s10845-016-1217-y ↗
18. Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength10.12989/scs.2016.21.3.679 ↗
19. Prediction of an environmental issue of mine blasting: An imperialistic competitive algorithm-based fuzzy system10.1007/s13762-017-1395-y ↗
20. Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system10.1007/s00366-018-0659-6 ↗
21. Developing a least squares support vector machine for estimating the blast-induced flyrock10.1007/s00366-017-0568-0 ↗
22. Classification of rockburst in underground projects: Comparison of ten supervised learning methods10.1061/(ASCE)CP.1943-5487.0000553 ↗
23. Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel10.1061/(ASCE)GM.1943-5622.0000817 ↗
24. Liao, X., Khandelwal, M., Yang, H., Koopialipoor, M., and Murlidhar, B.R. (2019). Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Eng. Comput., 1–12.10.1007/s00366-019-00711-6 ↗
25. Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories10.1061/(ASCE)CF.1943-5509.0001292 ↗
26. Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining10.1177/1077546314568172 ↗
27. Evaluation method of rockburst: State-of-the-art literature review10.1016/j.tust.2018.08.029 ↗
28. Evaluation of effect of blasting pattern parameters on back break using neural networks10.1016/j.ijrmms.2008.02.007 ↗
29. A new model based on gene expression programming to estimate air flow in a single rock joint10.1007/s12665-016-5524-6 ↗
30. Prediction of flyrock in open pit blasting operation using machine learning method10.1016/j.ijmst.2013.05.005 ↗
31. Correlating static properties of coal measures rocks with P-wave velocity10.1016/j.coal.2009.01.004 ↗
32. Application of improved ANFIS approaches to estimate bearing capacity of piles10.1007/s00500-018-3517-y ↗
33. Prediction of self-compacting concrete strength using artificial neural networks10.1080/19648189.2016.1246693 ↗
34. Compressive strength of natural hydraulic lime mortars using soft computing techniques10.1016/j.prostr.2019.08.122 ↗
35. Application of several optimization techniques for estimating TBM advance rate in granitic rocks10.1016/j.jrmge.2019.01.002 ↗
36. Xu, H., Zhou, J., GAsteris, P., Jahed Armaghani, D., and Tahir, M.M. (2019). Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate. Appl. Sci., 9.10.3390/app9183715 ↗
37. Soft computing-based techniques for concrete beams shear strength10.1016/j.prostr.2019.08.123 ↗
38. Asteris, P.G., and Nikoo, M. (2019). Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput. Appl., 1–11.10.1007/s00521-018-03965-1 ↗
39. Asteris, P.G., Moropoulou, A., Skentou, A.D., Apostolopoulou, M., Mohebkhah, A., Cavaleri, L., Rodrigues, H., and Varum, H. (2019). Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects. Appl. Sci., 9.10.3390/app9020243 ↗
40. Hajihassani, M., Abdullah, S.S., Asteris, P.G., and Armaghani, D.J. (2019). A Gene Expression Programming Model for Predicting Tunnel Convergence. Appl. Sci., 9.10.3390/app9214650 ↗
41. Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars
42. Cavaleri, L., Asteris, P.G., Psyllaki, P.P., Douvika, M.G., Skentou, A.D., and Vaxevanidis, N.M. (2019). Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks. Appl. Sci., 9.10.3390/app9142788 ↗
43. Prediction of the fundamental period of infilled RC frame structures using artificial neural networks10.1155/2016/5104907 ↗
44. Surface treatment of tool steels against galling failure. In MATEC Web of Conferences
45. Prediction of the compressive strength of self-compacting concrete using surrogate models
46. Koopialipoor, M., Tootoonchi, H., Armaghani, D.J., Mohamad, E.T., and Hedayat, A. (2019). Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull. Eng. Geol. Environ., 1–14.10.1007/s10064-019-01538-7 ↗
47. Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass10.1016/j.tust.2018.07.023 ↗
48. Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides10.1016/j.enggeo.2018.03.023 ↗
49. Investigation on the cracking character of jointed rock mass beneath TBM disc cutter10.1007/s00603-017-1395-8 ↗
50. Zhou, J., Koopialipoor, M., Murlidhar, B.R., Fatemi, S.A., Tahir, M.M., Armaghani, D.J., and Li, C. (2019). Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance. Nat. Resour. Res., 1–15.10.1007/s11053-019-09519-z ↗
51. Guo, H., Zhou, J., Koopialipoor, M., Armaghani, D.J., and Tahir, M.M. (2019). Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng. Comput., 1–14.10.1007/s00366-019-00816-y ↗
52. Predicting tunnel boring machine performance through a new model based on the group method of data handling10.1007/s10064-018-1349-8 ↗
53. Asteris, P., Roussis, P., and Douvika, M. (2017). Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors, 17.10.3390/s17061344 ↗
54. Modeling of Surface Roughness in Electro-Discharge Machining Using Artificial Neural Networks
55. Sarir, P., Chen, J., Asteris, P.G., Armaghani, D.J., and Tahir, M.M. (2019). Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng. Comput., 1–19.10.1007/s00366-019-00808-y ↗
56. Chen, H., Asteris, P.G., Jahed Armaghani, D., Gordan, B., and Pham, B.T. (2019). Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models. Appl. Sci., 9.10.3390/app9061042 ↗
57. Prediction of Uniaxial Compressive Strength, Tensile Strength and Porosity of Sedimentary Rocks Using Sound Level Produced During Rotary Drilling10.1007/s00603-011-0160-7 ↗
58. Overbreak prediction and optimization in tunnel using neural network and bee colony techniques10.1007/s00366-018-0658-7 ↗
59. Zhao, Y., Noorbakhsh, A., Koopialipoor, M., Azizi, A., and Tahir, M.M. (2019). A new methodology for optimization and prediction of rate of penetration during drilling operations. Eng. Comput., 1–9.10.1007/s00366-019-00715-2 ↗
60. Three hybrid intelligent models in estimating flyrock distance resulting from blasting10.1007/s00366-018-0596-4 ↗
61. A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls10.1007/s00366-018-0625-3 ↗
62. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions10.1007/s00500-018-3253-3 ↗
63. A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels10.1007/s10064-017-1116-2 ↗
64. Rock strength estimation: A PSO-based BP approach10.1007/s00521-016-2728-3 ↗
65. Atashpaz-Gargari, E., and Lucas, C. (2007, January 25–28). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), Singapore.10.1109/CEC.2007.4425083 ↗
66. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm10.1007/s10064-014-0657-x ↗
67. A combination of the ICA-ANN model to predict air-overpressure resulting from blasting10.1007/s00366-015-0408-z ↗
68. Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques10.1007/s00366-018-0642-2 ↗
69. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition10.1016/j.tust.2016.12.009 ↗
70. Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University, Engineering Faculty, Computer Engineering Department. Technical Report-tr06.
71. On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology10.1016/j.petlm.2015.11.004 ↗
72. The Application of Artificial Bee Colony and Gravitational Search Algorithm in Reservoir Optimization10.1007/s11269-016-1304-z ↗
73. An artificial bee colony approach for clustering10.1016/j.eswa.2009.11.003 ↗
74. Rodriguez, F.J., García-Martínez, C., Blum, C., and Lozano, M. (2012). An artificial bee colony algorithm for the unrelated parallel machines scheduling problem. International Conference on Parallel Problem Solving from Nature, Springer.10.1007/978-3-642-32964-7_15 ↗
75. Oliveira, I.M.S.D., Schirru, R., and de Medeiros, J. (October, January 27). On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a pwr nuclear power plant. Proceedings of the 2009 international nuclear Atlantic conference (INAC 2009), Rio de Janeiro, Brazil.
76. Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling10.1016/j.petrol.2011.05.006 ↗
77. Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm10.1007/s10064-015-0720-2 ↗
78. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm10.1007/s10898-007-9149-x ↗
79. Sun, L., Koopialipoor, M., Armaghani, D.J., Tarinejad, R., and Tahir, M.M. (2019). Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Eng. Comput., 1–13.10.1007/s00366-019-00875-1 ↗
80. Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN10.1007/s12665-019-8163-x ↗
81. A novel numerical optimization algorithm inspired from weed colonization10.1016/j.ecoinf.2006.07.003 ↗
82. Applying wavelets to short-term load forecasting using PSO-based neural networks10.1109/TPWRS.2008.2008606 ↗
83. Classification of mental task from EEG data using neural networks based on particle swarm optimization10.1016/j.neucom.2008.02.017 ↗
84. A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility10.1016/j.measurement.2013.04.077 ↗
85. Prediction of hard rock TBM penetration rate using particle swarm optimization10.1016/j.ijrmms.2011.02.013 ↗
86. Airblast prediction through a hybrid genetic algorithm-ANN model10.1007/s00521-016-2598-8 ↗
87. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN10.1016/j.measurement.2014.08.007 ↗
88. Integrative Discovery of Multifaceted Sequence Patterns by Frame-Relayed Search and Hybrid PSO-ANN
89. Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks
90. Self-compacting concrete strength prediction using surrogate models10.1007/s00521-017-3007-7 ↗
91. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods10.1007/s00366-015-0402-5 ↗
92. Multilayer feedforward networks are universal approximators10.1016/0893-6080(89)90020-8 ↗
93. An optimized ANN model based on genetic algorithm for predicting ripping production10.1007/s00521-016-2359-8 ↗
94. Neuro-fuzzy technique to predict air-overpressure induced by blasting10.1007/s12517-015-1984-3 ↗
95. Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation10.1016/j.ijrmms.2005.06.007 ↗
96. Intelligent design of retaining wall structures under dynamic conditions
97. Koopialipoor, M., Noorbakhsh, A., Noroozi Ghaleini, E., Jahed Armaghani, D., and Yagiz, S. (2019). A new approach for estimation of rock brittleness based on non-destructive tests. Nondestruct. Test. Eval., 1–22.10.1080/10589759.2019.1623214 ↗
98. Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique10.1109/ACCESS.2019.2927632 ↗
99. A new hybrid method for predicting ripping production in different weathering zones through in-situ tests10.1016/j.measurement.2019.07.054 ↗
100. Koopialipoor, M., Murlidhar, B.R., Hedayat, A., Armaghani, D.J., Gordan, B., and Mohamad, E.T. (2019). The use of new intelligent techniques in designing retaining walls. Eng. Comput., 1–2.10.1007/s00366-018-00700-1 ↗