A Case Study on the Numerical-Probability Approach for Deep Excavation Analysis

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Numerical-Probability Approach for Deep Excavation Analysis</title> <meta name="description" content="A Case Study on the Numerical-Probability Approach for Deep Excavation Analysis"> <meta name="keywords" content="Numerical probability modeling, deep excavation, allowable maximum displacement, finite difference method, FDM."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Case Study on the Numerical-Probability Approach for Deep Excavation Analysis"> <meta name="citation_author" content="Komeil Valipourian"> <meta name="citation_publication_date" content="2020/07/02"> <meta name="citation_journal_title" content="International Journal of Geotechnical and Geological Engineering"> <meta name="citation_volume" content="14"> <meta name="citation_issue" content="8"> <meta name="citation_firstpage" content="216"> <meta name="citation_lastpage" content="224"> 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<p>Urban advances and the growing need for developing infrastructures has increased the importance of deep excavations. In this study, after the introducing probability analysis as an important issue, an attempt has been made to apply it for the deep excavation project of Bangkok’s Metro as a case study. For this, the numerical probability model has been developed based on the Finite Difference Method and Monte Carlo sampling approach. The results indicate that disregarding the issue of probability in this project will result in an inappropriate design of the retaining structure. Therefore, probabilistic redesign of the support is proposed and carried out as one of the applications of probability analysis. A 50% reduction in the flexural strength of the structure increases the failure probability just by 8% in the allowable range and helps improve economic conditions, while maintaining mechanical efficiency. With regard to the lack of efficient design in most deep excavations, by considering geometrical and geotechnical variability, an attempt was made to develop an optimum practical design standard for deep excavations based on failure probability. On this basis, a practical relationship is presented for estimating the maximum allowable horizontal displacement, which can help improve design conditions without developing the probability analysis.</p> <iframe src="https://publications.waset.org/10011374.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Numerical%20probability%20modeling" title="Numerical probability modeling">Numerical probability modeling</a>, <a href="https://publications.waset.org/search?q=deep%20excavation" title=" deep excavation"> deep excavation</a>, <a href="https://publications.waset.org/search?q=allowable%20maximum%20displacement" title=" allowable maximum displacement"> allowable maximum displacement</a>, <a href="https://publications.waset.org/search?q=finite%20difference%20method" title=" finite difference method"> finite difference method</a>, <a href="https://publications.waset.org/search?q=FDM." title=" FDM."> FDM.</a> </p> <a href="https://publications.waset.org/10011374/a-case-study-on-the-numerical-probability-approach-for-deep-excavation-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011374/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011374/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011374/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011374/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011374/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011374/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011374/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011374/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011374/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011374/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">692</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Ghahramani, Z., Probabilistic machine learning and artificial intelligence. 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A Case Study on the Numerical-Probability Approach for Deep Excavation Analysis