An improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation
An improved artificial bee colony-random forest (IABC-RF) model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit. A new search strategy of the artificial bee colony (ABC) algorithm is herein developed and incorporated, with the results showing tha...
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| Published in: | Underground space (Beijing) Vol. 7; no. 4; pp. 514 - 527 |
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01-08-2022
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| Abstract | An improved artificial bee colony-random forest (IABC-RF) model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit. A new search strategy of the artificial bee colony (ABC) algorithm is herein developed and incorporated, with the results showing that a much higher computational efficiency can be achieved with the new model, while high computational accuracy can also be maintained. The improved ABC algorithm is thereafter utilised and combined with the random forest (RF) model, where four important hyper-parameters are optimized, for a tunnel deformation prediction. Results are thoroughly compared with those of other prediction methods based on machine learning (ML), as well as the monitored data on the site. Via the comparisons, the validity and effectiveness of the proposed model are fully demonstrated, and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering. |
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| AbstractList | An improved artificial bee colony-random forest (IABC-RF) model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit. A new search strategy of the artificial bee colony (ABC) algorithm is herein developed and incorporated, with the results showing that a much higher computational efficiency can be achieved with the new model, while high computational accuracy can also be maintained. The improved ABC algorithm is thereafter utilised and combined with the random forest (RF) model, where four important hyper-parameters are optimized, for a tunnel deformation prediction. Results are thoroughly compared with those of other prediction methods based on machine learning (ML), as well as the monitored data on the site. Via the comparisons, the validity and effectiveness of the proposed model are fully demonstrated, and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering. |
| Author | Wang, Chaoran Feng, Tugen Zhang, Jian Jin, Yin-Fu Wang, Bin |
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| Cites_doi | 10.1016/j.knosys.2019.04.015 10.1007/s10064-015-0720-2 10.1007/s10898-007-9149-x 10.1016/j.ress.2020.107186 10.1007/s00500-018-3253-3 10.1016/j.undsp.2019.12.001 10.1007/s10064-020-02057-6 10.32604/cmc.2020.013813 10.1109/ACCESS.2021.3049578 10.1016/j.apor.2020.102223 10.1016/j.undsp.2019.12.003 10.1007/s10732-008-9080-4 10.1038/s41598-020-76569-2 10.1016/j.autcon.2019.102860 10.1007/s11709-019-0561-3 10.1007/s00521-018-03965-1 10.1016/j.enconman.2017.02.006 10.3390/su12010232 10.1016/j.egyr.2020.11.271 10.1016/j.engfailanal.2021.105784 10.1007/978-981-15-7984-4_17 10.4028/www.scientific.net/AMM.256-259.1157 10.1016/j.engfailanal.2020.104832 10.1016/j.gsf.2019.12.003 |
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| References | Huang (10.1016/j.undsp.2021.11.004_b0040) 2020; 10 Feng (10.1016/j.undsp.2021.11.004_b0030) 2021; 1–19 García (10.1016/j.undsp.2021.11.004_b0035) 2009; 15 Assiri (10.1016/j.undsp.2021.11.004_b0005) 2020; 66 Zhang (10.1016/j.undsp.2021.11.004_b0120) 2021; 130 Su (10.1016/j.undsp.2021.11.004_b0085) 2021; 9 Zhang (10.1016/j.undsp.2021.11.004_b0115) 2020; 118 Liu (10.1016/j.undsp.2021.11.004_b0065) 2020; 2020 Wei (10.1016/j.undsp.2021.11.004_b0095) 2012; 256–259 Zheng (10.1016/j.undsp.2021.11.004_b0145) 2020; 1–21 Karaboga (10.1016/j.undsp.2021.11.004_b0055) 2007; 39 Utkin (10.1016/j.undsp.2021.11.004_b0090) 2019; 177 Ebrahimi (10.1016/j.undsp.2021.11.004_b0025) 2016; 75 Koopialipoor (10.1016/j.undsp.2021.11.004_b0060) 2019; 23 Zhang (10.1016/j.undsp.2021.11.004_b0135) 2020; 11 Zhang (10.1016/j.undsp.2021.11.004_b0125) 2019; 106 Yokoyama (10.1016/j.undsp.2021.11.004_b0110) 2020; 6 Shahrour (10.1016/j.undsp.2021.11.004_b0075) 2021; 6 Ibrahim (10.1016/j.undsp.2021.11.004_b0050) 2017; 138 Liu (10.1016/j.undsp.2021.11.004_b0070) 2021; 80 Chen (10.1016/j.undsp.2021.11.004_b0020) 2019; 13 Xu (10.1016/j.undsp.2021.11.004_b0100) 2021; 48 Simsekler (10.1016/j.undsp.2021.11.004_b0080) 2020; 204 Yan (10.1016/j.undsp.2021.11.004_b0105) 2020; 12 Asteris (10.1016/j.undsp.2021.11.004_b0010) 2019; 31 Zhang (10.1016/j.undsp.2021.11.004_b0140) 2021; 6 Huo (10.1016/j.undsp.2021.11.004_b0045) 2020; 1258 Zhang (10.1016/j.undsp.2021.11.004_b0130) 2020; 101 Birattari (10.1016/j.undsp.2021.11.004_b0015) 2010; 2010 |
| References_xml | – volume: 177 start-page: 136 year: 2019 ident: 10.1016/j.undsp.2021.11.004_b0090 article-title: A weighted random survival forest publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.04.015 contributor: fullname: Utkin – volume: 75 start-page: 27 issue: 1 year: 2016 ident: 10.1016/j.undsp.2021.11.004_b0025 article-title: Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm publication-title: Bulletin of Engineering Geology and the Environment doi: 10.1007/s10064-015-0720-2 contributor: fullname: Ebrahimi – volume: 39 start-page: 459 issue: 3 year: 2007 ident: 10.1016/j.undsp.2021.11.004_b0055 article-title: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm publication-title: Journal of Global Optimization doi: 10.1007/s10898-007-9149-x contributor: fullname: Karaboga – volume: 204 start-page: 107186 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0080 article-title: Evaluation of patient safety culture using a random forest algorithm publication-title: Reliability Engineering & System Safety doi: 10.1016/j.ress.2020.107186 contributor: fullname: Simsekler – volume: 23 start-page: 5913 issue: 14 year: 2019 ident: 10.1016/j.undsp.2021.11.004_b0060 article-title: Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions publication-title: Soft Computing doi: 10.1007/s00500-018-3253-3 contributor: fullname: Koopialipoor – volume: 6 start-page: 233 issue: 3 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0075 article-title: Use of soft computing techniques for TBM tunnelling optimisation publication-title: Underground Space doi: 10.1016/j.undsp.2019.12.001 contributor: fullname: Shahrour – volume: 80 start-page: 2283 issue: 3 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0070 article-title: Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data publication-title: Bulletin of Engineering Geology and the Environment doi: 10.1007/s10064-020-02057-6 contributor: fullname: Liu – volume: 2010 start-page: 311 year: 2010 ident: 10.1016/j.undsp.2021.11.004_b0015 article-title: F-Race and Iterated F-Race: An Overview. In Experimental Methods for the Analysis of Optimization Algorithms publication-title: Berlin, Heidelberg: Springer, Berlin Heidelberg contributor: fullname: Birattari – volume: 66 start-page: 767 issue: 1 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0005 article-title: Anomaly classification using genetic algorithm-based random forest model for network attack detection publication-title: Computers, Materials & Continua doi: 10.32604/cmc.2020.013813 contributor: fullname: Assiri – volume: 9 start-page: 9142 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0085 article-title: An improved random forest model for the prediction of dam displacement publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3049578 contributor: fullname: Su – volume: 1–19 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0030 article-title: Prediction of stratum deformation during the excavation of a foundation pit in composite formation based on the artificial bee colony–back-propagation model publication-title: Engineering Optimization contributor: fullname: Feng – volume: 101 start-page: 102223 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0130 article-title: Random forest based artificial intelligent model for predicting failure envelopes of caisson foundations in sand publication-title: Applied Ocean Research doi: 10.1016/j.apor.2020.102223 contributor: fullname: Zhang – volume: 6 start-page: 353 issue: 4 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0140 article-title: Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunnelling publication-title: Underground Space doi: 10.1016/j.undsp.2019.12.003 contributor: fullname: Zhang – volume: 15 start-page: 617 issue: 6 year: 2009 ident: 10.1016/j.undsp.2021.11.004_b0035 article-title: A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: A case study on the CEC'2005 special session on real parameter optimization publication-title: Journal of Heuristics doi: 10.1007/s10732-008-9080-4 contributor: fullname: García – volume: 10 start-page: 19397 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0040 article-title: A combination of fuzzy delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting publication-title: Scientific Reports doi: 10.1038/s41598-020-76569-2 contributor: fullname: Huang – volume: 106 start-page: 102860 year: 2019 ident: 10.1016/j.undsp.2021.11.004_b0125 article-title: Real-time analysis and regulation of EPB shield steering using random forest publication-title: Automation in Construction doi: 10.1016/j.autcon.2019.102860 contributor: fullname: Zhang – volume: 13 start-page: 1363 issue: 6 year: 2019 ident: 10.1016/j.undsp.2021.11.004_b0020 article-title: Prediction of shield tunneling-induced ground settlement using machine learning techniques publication-title: Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-019-0561-3 contributor: fullname: Chen – volume: 31 start-page: 4837 issue: 9 year: 2019 ident: 10.1016/j.undsp.2021.11.004_b0010 article-title: Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures publication-title: Neural Computing and Applications doi: 10.1007/s00521-018-03965-1 contributor: fullname: Asteris – volume: 138 start-page: 413 year: 2017 ident: 10.1016/j.undsp.2021.11.004_b0050 article-title: A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2017.02.006 contributor: fullname: Ibrahim – volume: 12 start-page: 232 issue: 1 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0105 article-title: Tunnel surface settlement forecasting with ensemble learning publication-title: Sustainability doi: 10.3390/su12010232 contributor: fullname: Yan – volume: 1–21 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0145 article-title: Random forest method-based prediction and control of bridge pier displacements during construction of two overlapped EPBM tunnels publication-title: European Journal of Environmental and Civil Engineering contributor: fullname: Zheng – volume: 6 start-page: 150 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0110 article-title: Comparison between ANN and random forest for leakage current alarm prediction publication-title: Energy Reports doi: 10.1016/j.egyr.2020.11.271 contributor: fullname: Yokoyama – volume: 130 start-page: 105784 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0120 article-title: Evaluation and analysis of the causes of a landslide and treatment measures during the excavation of a tunnel through a soil-rock interface publication-title: Engineering Failure Analysis doi: 10.1016/j.engfailanal.2021.105784 contributor: fullname: Zhang – volume: 1258 start-page: 216 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0045 article-title: Improved random forest algorithm based on adaptive step size artificial bee colony optimization. Data Science. ICPCSEE 2020 publication-title: Communications in Computer and Information Science doi: 10.1007/978-981-15-7984-4_17 contributor: fullname: Huo – volume: 256–259 start-page: 1157 year: 2012 ident: 10.1016/j.undsp.2021.11.004_b0095 article-title: Prediction of the deformation of the surrounding rock around tunnels by GA-Bp network model publication-title: Applied Mechanics and Materials doi: 10.4028/www.scientific.net/AMM.256-259.1157 contributor: fullname: Wei – volume: 48 start-page: 153 issue: 3 year: 2021 ident: 10.1016/j.undsp.2021.11.004_b0100 article-title: A Simplified Calculation Method for Horizontal Displacement publication-title: Journal of Hunan University (Natural Sciences) contributor: fullname: Xu – volume: 118 start-page: 104832 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0115 article-title: Investigation of the cause of shield-driven tunnel instability in soil with a soft upper layer and hard lower layer publication-title: Engineering Failure Analysis doi: 10.1016/j.engfailanal.2020.104832 contributor: fullname: Zhang – volume: 2020 start-page: 8863425 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0065 article-title: Gas outburst prediction model using improved entropy weight grey correlation analysis and IPSO-LSSVM publication-title: Mathematical Problems in Engineering contributor: fullname: Liu – volume: 11 start-page: 1095 issue: 4 year: 2020 ident: 10.1016/j.undsp.2021.11.004_b0135 article-title: State-of-the-art review of soft computing applications in underground excavations publication-title: Geoscience Frontiers doi: 10.1016/j.gsf.2019.12.003 contributor: fullname: Zhang |
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| Title | An improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation |
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