Resource scheduling and planning for tunneling with a new resource model of the Decision Aids for Tunneling (DAT)

Min, Sang-Yoon; Einstein, Herbert H.

doi:10.1016/j.tust.2015.10.038

Cited by 24 publications

(4 citation statements)

References 17 publications

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“…Liu et al [35] proposed a "collapse warning value" for soft rock tunnel construction according to relevant regulations, engineering experience, and field monitoring to guarantee the safety of the construction area. Min and Einstein [36] utilized tunnel engineering risk management to assist decision-making systems in simulating resource scheduling and planning through tunnel construction. By formulating varying tunnel construction strategies, they achieved optimization of the construction process, considering events, costs, and resources.…”

Section: Construction Safety Risk Assessmentmentioning

confidence: 99%

Construction Safety Risk Assessment and Early Warning of Nearshore Tunnel Based on BIM Technology

Wu,

Yang,

et al. 2023

JMSE

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The challenging nature of nearshore tunnel construction environments introduces a multitude of potential hazards, consequently escalating the likelihood of incidents such as water influx. Existing construction safety risk management methodologies often depend on subjective experiences, leading to inconsistent reliability in assessment outcomes. The multifaceted nature of construction safety risk factors, their sources, and structures complicate the validation of these assessments, thus compromising their precision. Moreover, risk assessments generally occur pre-construction, leaving on-site personnel incapable of recommending pragmatic mitigation strategies based on real-time safety issues. To address these concerns, this paper introduces a construction safety risk assessment approach for nearshore tunnels based on multi-data fusion. In addressing the issue of temporal effectiveness when the conflict factor K in traditional Dempster–Shafer (DS) evidence theory nears infinity, the confidence Hellinger distance is incorporated for improvement. This is designed to accurately demonstrate the degree of conflict between two evidence chains. Subsequently, an integrated evaluation of construction safety risks for a specific nearshore tunnel in Ningbo is conducted through the calculation of similarity, support degree, and weight factors. Simultaneously, the Revit secondary development technology is utilized to visualize risk monitoring point warnings. The evaluation concludes that monitoring point K7+860 exhibits a level II risk, whereas other monitoring points maintain a normal status.

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Section: Construction Safety Risk Assessmentmentioning

confidence: 99%

Construction Safety Risk Assessment and Early Warning of Nearshore Tunnel Based on BIM Technology

Wu,

Yang,

et al. 2023

JMSE

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“…Chronographic modeling proposes an internal division of activities related to internal or external scales [8]. External and internal scales can be vertical or horizontal to designate the measuring unit of the orthogonal axis system (Fig.…”

Section: B Attribute By Scalementioning

confidence: 99%

“…Using simulation techniques, Chen et al [7] proposed an intelligent scheduling system (ISS) to distribute resources and assign different levels of priorities to different activities. Min and Einstein [8] employed simulation techniques to resource/space availability to develop a resource model applicable in Tunnel construction. Siu et al [9] proposed resourcebased scheduling as a solution to the supply-demand matching problem.…”

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confidence: 99%

Chronographic Allocation Techniques for Construction Projects

Francis¹

2017

JCES

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Abstract-The literature is abundant in terms of research on resource management and optimization methods. Many deterministic, probabilistic and heuristic methods propose how to use resources efficiently. However, this paper discusses allocation techniques, i.e. how to technically allocate resources to activities rather than how to use resources efficiently. Though this subject is thoroughly covered by commercial scheduling software, it is generally absent from the literature. This paper compares the proposed Chronographic Modeling with the methods employed by commercial scheduling software. The new allocation methodology defines three attribute methods: complete attribute, attribute by segment and attribute by scale. Proposing internal divisions, as well as internal, external, vertical and horizontal scales, allows planners to create all kinds of attributes, from bulk allocation to any type of external and internal scale attribute. The links between scales could also show interactions between different measurement units and offer several types of durations, quantities and costs for increased flexibility and resolution of the limitations of existing methods. This paper adds to the body of knowledge by introducing a complete and realistic allocation methodology for construction project scheduling.

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“…Arsalan Mahmoodzadeh et al 40 have been developed hybrid algorithms to predict Mode-I rock fracture toughness and fivefold cross-validation was applied. Besides, researchers also proposed different approaches to forecast the construction time and costs of tunneling projects, such as statistical methods 41 – 44 , fault tree or event tree analysis, decision trees or risk matrixes 45 – 47 . Arsalan Mahmoodzadeh et al 48 using four machine learning methods including the linear regression (LR), Gaussian process regression (GPR), support vector regression (SVR), and decision tree (DT) with the grey wolf optimization (GWO) algorithm was used to fine-tune the models' hyper-parameters to predict geology, duration, and costs of tunnels construction.…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent displacement prediction model of karst tunnels

Zhao

Ding

et al. 2022

Sci Rep

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Karst is a common engineering environment in the process of tunnel construction, which poses a serious threat to the construction and operation, and the theory on calculating the settlement without the assumption of semi-infinite half-space is lack. Meanwhile, due to the limitation of test conditions or field measurement, the settlement of high-speed railway tunnel in Karst region is difficult to control and predict effectively. In this study, a novel intelligent displacement prediction model, following the machine learning (ML) incorporated with the finite difference method, is developed to evaluate the settlement of the tunnel floor. A back propagation neural network (BPNN) algorithm and a random forest (RF) algorithm are used herein, while the Bayesian regularization is applied to improve the BPNN and the Bayesian optimization is adopted for tuning the hyperparameters of RF. The newly proposed model is employed to predict the settlement of Changqingpo tunnel floor, located in the southeast of Yunnan Guizhou Plateau, China. Numerical simulations have been performed on the Changqingpo tunnel in terms of variety of karst size, and locations. Validations of the numerical simulations have been validated by the field data. A data set of 456 samples based on the numerical results is constructed to evaluate the accuracy of models’ predictions. The correlation coefficients of the optimum BPNN and BR model in testing set are 0.987 and 0.925, respectively, indicating that the proposed BPNN model has more great potential to predict the settlement of tunnels located in karst areas. The case study of Changqingpo tunnel in karst region has demonstrated capability of the intelligent displacement prediction model to well predict the settlement of tunnel floor in Karst region.

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Resource scheduling and planning for tunneling with a new resource model of the Decision Aids for Tunneling (DAT)

Abstract: 11Resource scheduling and planning are the strategies required to determine the sequence of

Cited by 24 publications

References 17 publications

Construction Safety Risk Assessment and Early Warning of Nearshore Tunnel Based on BIM Technology

Construction Safety Risk Assessment and Early Warning of Nearshore Tunnel Based on BIM Technology

Chronographic Allocation Techniques for Construction Projects

A novel intelligent displacement prediction model of karst tunnels

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