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Steel and Composite Structures Volume 41, Number 6, December25 2021 , pages 831-850 DOI: https://doi.org/10.12989/scs.2021.5.41.831 |
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Moment-rotational analysis of soil during mining induced ground movements by hybrid machine learning assisted quantification models of ELM-SVM |
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Bibo Dai, Zhijun Xu, Jie Zeng, Yousef Zandi, Abouzar Rahimi, Sara Pourkhorshidi, Mohamed Amine Khadimallah, Xingdong Zhao and Islam Ezz El-Arab
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| Abstract | ||
| Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winkler soil model, analytical equations for the moment–rotation response of soil during mining induced ground movements are developed. To define the full static moment–rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment–rotation curve. The maximal moment–rotation response of the footing is defined by X=0:6. Despite the use of Winkler model, the computed moment–rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed. | ||
| Key Words | ||
| d ELM- SVM; induced ground; machine learning; moment rotation, mining; soil interaction | ||
| Address | ||
| Bibo Dai:School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China Zhijun Xu:School of Civil Engineering, Henan University of Technology, Zhengzhou, China Jie Zeng:Chongqing Jianzhu College, Academy of Traffic and Municipal Engineering, 857 Lihua Avenue, Nan'an District, Chongqing, 400072, China Yousef Zandi:Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran Abouzar Rahimi:Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran Sara Pourkhorshidi:Civil Engineering Department, Sahand University of Technology, Tabriz, Iran Mohamed Amine Khadimallah:Prince Sattam Bin Abdulaziz University, College of Engineering, Civil Engineering Department, Al-Kharj, 16273, Saudi Arabia/ Laboratory of Systems and Applied Mechanics, Polytechnic School of Tunisia, University of Carthage, Tunis, Tunisia Xingdong Zhao:School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China Islam Ezz El-Arab:Structural engineering, Faculty of engineering, Tanta University, Egypt | ||