Predicting Safety Risks in Deep Foundation Pits in Subway Infrastructure Projects: Support Vector Machine Approach

Zhou, Ying; Su, Wan-Jun; Ding, Lieyun; Luo, Hanbin; Love, Peter E.D.

doi:10.1061/(asce)cp.1943-5487.0000700

Cited by 95 publications

(38 citation statements)

References 36 publications

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“…Thus, m was chosen as 4 for the following analyses. Furthermore, assuming that the displacement limit R (x) is 73.5 mm, Ω (x) can be determined according to Equation (4). The derivative of Ω(x) giveṡ Ω(x).…”

Section: Deformation Reliability Assessmentmentioning

confidence: 99%

“…The past decades have witnessed rapid growth in the number and scale of deep foundation pit projects in subway constructions around the world. Once a foundation pit accident (e.g., collapse of retaining structures) occurs, the consequence is often catastrophic, with ripple effects to the safety of adjacent buildings and underground pipelines [1][2][3][4]. In order to prevent such accidents, an important goal in the design and construction of foundation pits is to control the deformation of the soil and the retaining structures.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Deformation Reliability of Retaining Structures in Deep Excavations Considering Measurement Error

Wang

et al. 2019

Applied Sciences

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The deflections of retaining structures during deep excavation should be controlled below a predefined threshold in an attempt to mitigate the deformation-posed damage to the surrounding buildings and infrastructure facilities. In this paper, a new approach is presented to conducting deformation reliability assessments of retaining structures. The method is based on an extension of the classical first-passage reliability problem and is applied on the spatial scale of the retaining structures. With the proposed method, the in situ monitoring data and the associated measurement error can be easily incorporated, which accordingly improves the assessment accuracy. Moreover, the proposed method has a closed-form solution, which is beneficial for the computation efficiency and its practical application. The method was applied to assess the retaining structure reliability in a foundation excavation of a subway station in Fuzhou, China. The accuracy of the analytical results was verified through a comparison with those of Monte Carlo simulation. It was shown that the proposed method can represent well, the deformation-based safety level of retaining structures.

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Section: Deformation Reliability Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Deformation Reliability of Retaining Structures in Deep Excavations Considering Measurement Error

Wang

et al. 2019

Applied Sciences

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“…Whilst it is not the intention of this paper to review all such efforts, we found it pertinent to provide some examples. There are considerable evidences of SVM's varied application such as predicting medication adherence in heart failure patients [10], detection of epileptic electroencephalogram [11], financial distress and risk prediction [12,13], construction safety-risk assessment [14,15] , revenue forecasting [16], forecasting wind speed for wind farms [17], groundwater simulation [18] or apple disease detection [19]. The above examples not only illustrate the popularity of SVM across various fields, but also its competence at providing comparatively accurate predictions and classifications.…”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England

Hassani

Silva

Combe

et al. 2019

Stats

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Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully.

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“…After nearly half a year of research and analysis, most accidents are due to safety hazards without timely treatment (Axelsson, 2017;Yonge et al, 2017;Zhou et al, 2014). The enterprise department expects the new intelligent, security risks awareness, and decision-making early warning systems (Erban and Gorelick, 2016;Oppliger et al, 2017;Zhou et al, 2017;Viana and Sato, 2014), which can automatically extract semantic feature information, and intelligently conduct security risk decision analysis, to prevent and reduce the occurrence of security incidents in advance.…”

Section: Introductionmentioning

confidence: 99%

A decision system based on active perception and intelligent analysis for key location security information

Chen

Cao

et al. 2019

IJCSE

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In various enterprises, the security data are manually entered by multiple security workers at different times, which can lead to disordered data in the system, and also, analysis decision files need to be manually processed. To solve these problems, this paper presents a decision system for key location security information based on active perception and intelligent analysis. First, the designed system is developed based on C/S framework. Then, a deep learning perception model is constructed. Experimental results show that this system can significantly reduce the heavy workload of security inspectors, and reduce the frequency of safety accidents.

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Predicting Safety Risks in Deep Foundation Pits in Subway Infrastructure Projects: Support Vector Machine Approach

Cited by 95 publications

References 36 publications

Data-Driven Deformation Reliability of Retaining Structures in Deep Excavations Considering Measurement Error

Data-Driven Deformation Reliability of Retaining Structures in Deep Excavations Considering Measurement Error

A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England

A decision system based on active perception and intelligent analysis for key location security information

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