Probabilistic approach to assessing and monitoring settlements caused by tunneling

Camós, Carles; Špačková, Olga; Straub, Daniel; Molins, Climent

doi:10.1016/j.tust.2015.10.041

Cited by 32 publications

(9 citation statements)

References 12 publications

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“…Khodakarami and Abdi (2014) proposed a quantitative risk evaluation structure using Bayesian networks for probabilistic risk analysis. Similarly, a BBN model for the settlement of buildings and other adjacent structures is introduced by Camós et al (2016). Qazi et al (2016) designed a project complexity and risk model by using BBNs for risk assessment in infrastructure projects.…”

Section: Applications Of Ai Methodsmentioning

confidence: 99%

A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies

Afzal

Shao

Nazir

et al. 2019

IJMPB

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Purpose In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty. Design/methodology/approach This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system. Findings The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty. Research limitations/implications This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis. Practical implications These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice. Originality/value This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

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Section: Applications Of Ai Methodsmentioning

confidence: 99%

A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies

Afzal

Shao

Nazir

et al. 2019

IJMPB

View full text Add to dashboard Cite

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“…Shield tunneling is often fraught with inevitable uncertainties that include, but not limited to, (1) the inherent variability of soils, (2) the measurement errors in quantifying the soil properties by field and laboratory tests, and (3) the limited knowledge on geotechnical conditions and simplified geotechnical models in predicting the ground settlements (e.g., [1,21]). ese uncertainties can be reduced by incorporating the field observation data into a Bayesian back analysis by estimating the posterior joint probability density function (PDF) of geomechanical parameters (e.g., [9,22]).…”

Section: Bus-based Sequential Probabilistic Back Analysis Approachmentioning

confidence: 99%

“…In order to alleviate the pressure of urban traffic, constructing underground rail transit network has become a main development of urban transportation system. Urban metro shield tunnel construction often leads to surrounding ground movements and further endangers adjacent structures, which, in turn, poses great threat to the tunnel itself (e.g., [1,2]). To mitigate the adverse impact of shield tunnel construction on the surrounding environment, the geomechanical parameters that can reflect the performance of a specific construction site and help to analyze the shield tunneling-induced ground subsidence shall be rationally determined (e.g., [3,4]).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling‐Induced Ground Settlements

Jiang

et al. 2020

Advances in Civil Engineering

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This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.

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“…Nevertheless, their effect cannot be taken into account with the presented methodology. All the proposed models in Sections 2, 3 and 4 can be used in combination with reliability techniques to take into account the uncertainty regarding the settlement trough models and the building response, as shown in Camós, Špačková et al (2014).…”

Section: Influence Of the Tunnel Face Location And Alignment Onmentioning

confidence: 99%

3D analytical prediction of building damage due to ground subsidence produced by tunneling

Camós

Molins

2015

Tunnelling and Underground Space Technology

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Tunnel construction entails the generation of ground settlements, which can endanger the adjacent buildings. The prediction of damages in buildings is usually based on the classical Gaussian profiles for the approximation of the subsidence trough and the equivalent beam method for modeling the response of building walls. Current available expressions refer to walls aligned transversally with respect to the tunnel axis, which usually represents the worst-case scenario. However, approximations must be done for other building alignments, since no analytical expressions are available for these cases. We propose a novel equation for the determination of the horizontal ground strain, which departs from the equations of the classical Gaussian settlement profiles. The novel formulation allows the application of the equivalent beam method in 3D and the modeling of the tunnel advance. The results show significant variations of the estimated damage depending on the wall position with respect to the tunnel axis. The paper reviews also certain relevant aspects of building damage predictions, such as the influence area of settlements and the possible contribution of ground horizontal strain to damage reduction. A parametric analysis is further performed to create a non-linear regression model that allows direct estimation of the maximum tensile strain in a building wall according to input values of geological conditions and wall and tunnel geometries.Peer ReviewedPostprint (author's final draft

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Probabilistic approach to assessing and monitoring settlements caused by tunneling

Cited by 32 publications

References 12 publications

A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies

A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies

Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling‐Induced Ground Settlements

3D analytical prediction of building damage due to ground subsidence produced by tunneling

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