Risk warning technologies and emergency response mechanisms in Sichuan–Tibet Railway construction

Safety is one of the most critical themes in any large-scale railway construction project. Recognizing the importance of safety in railway engineering, practitioners and researchers have proposed various standards and procedures to ensure safety in construction activities. In this study, we first review four critical research areas of risk warning technologies and emergency response mechanisms in railway construction, namely, (i) risk identification methods of large-scale railway construction projects, (ii) risk management of large-scale railway construction, (iii) emergency response planning and management, and (iv) emergency response and rescue mechanisms. After reviewing the existing studies, we present four corresponding research areas and recommendations on the Sichuan–Tibet Railway construction. This study aims to inject new significant theoretical elements into the decision-making process and construction of this railway project in China.     

Robust energy-efficient train speed profile optimization in a scenario-based position–time–speed network

Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit systems. Different from most existing studies that assume deterministic parameters as model inputs, this paper proposes a robust energy-efficient train speed profile optimization approach by considering the uncertainty of train modeling parameters. Specifically, we first construct a scenario-based position–time–speed (PTS) network by considering resistance parameters as discrete scenario-based random variables. Then, a percentile reliability model is proposed to generate a robust train speed profile, by which the scenario-based energy consumption is less than the model objective value at \alpha confidence level. To solve the model efficiently, we present several algorithms to eliminate the infeasible nodes and arcs in the PTS network and propose a model reformulation strategy to transform the original model into an equivalent linear programming model. Lastly, on the basis of our field test data collected in Beijing metro Yizhuang line, a series of experiments are conducted to verify the effectiveness of the model and analyze the influences of parameter uncertainties on the generated train speed profile.     

Cyber–physical systems development for construction applications

Cyber–physical systems (CPS) are intended to facilitate the tight coupling of the cyber and physical worlds. Their potential for enhancing the delivery and management of constructed facilities is now becoming understood. In these systems, it is vital to ensure bi-directional consistency between construction components and their digital replicas. This paper introduces the key features of CPS and describes why they are ideally suited for addressing a number of problems in the delivery of construction projects. It draws on examples of research prototypes developed using surveys, field experiments, and prototyping methodologies, to outline the key features and benefits of CPS for construction applications and the approach to their development. In addition, it outlines the lessons learned from developing various systems for the design, construction and management of constructed facilities, which include building component placement and tracking, temporary structures monitoring, and mobile crane safety. The paper concludes that the construction industry stands to reap numerous benefits from the adoption of CPS. It states that the future direction of CPS in construction will be driven by technological developments and the extent to which CPS is deployed in new application areas.     

Real option-based optimization for financial incentive allocation in infrastructure projects under public–private partnerships

Financial incentives that stimulate energy investments under public–private partnerships are considered scarce public resources, which require deliberate allocation to the most economically justified projects to maximize the social benefits. This study aims to solve the financial incentive allocation problem through a real option-based nonlinear integer programming approach. Real option theory is leveraged to determine the optimal timing and the corresponding option value of providing financial incentives. The ambiguity in the evolution of social benefits, the decision-maker’s attitude toward ambiguity, and the uncertainty in social benefits and incentive costs are all considered. Incentives are offered to the project portfolio that generates the maximum total option value. The project portfolio selection is formulated as a stochastic knapsack problem with random option values in the objective function and random incentive costs in the probabilistic budget constraint. The linear probabilistic budget constraint is subsequently transformed into a nonlinear deterministic one. Finally, the integer non-linear programming problem is solved, and the optimality gap is computed to assess the quality of the optimal solution. A case study is presented to illustrate how the limited financial incentives can be optimally allocated under uncertainty and ambiguity, which demonstrates the efficacy of the proposed method.     

Investigation of embankment with temperature-controlled ventilation along the Qinghai–Tibet Railway

The temperature-controlled ventilated embankment (TCVE) is an improved engineering measure developed based on the duct-ventilated embankment (DVE). The improved cooling effect of the TCVE is a result of enhanced heat exchange through controlling air convection in the ventilated ducts of the DVE. The field observational data from a TCVE testing section at Beiluhe, about midway in the Qinghai–Tibet Railway permafrost zone, indicates that restricting the air convection in the ducts during warm season can reduce the embankment heat absorption in the embankment. The results also indicate that the temperature in the wells of the ducts of the TCVE was about 1.0 °C lower than the temperature in the walls of the ducts of DVE. Especially it was found that in the seasonal ground temperature-decreasing period, the temperature of the permafrost at a depth of 3.5 m below the base of the embankment in TCVE decreased faster than at the same depth below the DVE. Two years after the TCVE was implemented, the temperature at a depth of 3.5 m below the embankment base was found 0.45 °C lower than at the same location below the DVE. Thermal modeling of the embankments shows that the mean annual heat loss of a TCVE is about twice that with the DVE, which means a doubled cooling effect of TCVE over DVE if think in this way. In addition, the cooling period with a TCVE is about 40 days longer than with a DVE, which contributes to its cooling effects.     

A framework for improving the efficiency of operational testing through Bayesian adaptive design

When developing a product, it is important to consider product performance from a user perspective. This type of evaluation can be done through operational testing—assessing the ability of representative users to satisfactorily accomplish tasks with the product in operationally representative environments. This process can be expensive and time-consuming, but is critical to understanding whether the product can adequately do the job for which it was designed. We show how an existing design of experiments (DOEs) process for operational testing can be leveraged to construct a Bayesian adaptive design. This design, nested within the larger design created by the DOE process, allows interim analyses to stop testing early for success or futility. Representative simulations are presented to demonstrate how these interim analyses can be used in an operational test setting, and reductions in necessary test events are shown. The application of Bayesian-adaptive design methods will allow future operational testing to be conducted in less time and at less expense, on average, without compromising the ability of the existing process to verify the product meets the user's needs.     

A Parallel Strategy for the Logical‐probabilistic Calculus‐based Method to Calculate Two‐terminal Reliability

The theory of network reliability has been applied to many complicated network structures, such as computer and communication networks, piping systems, electricity networks, and traffic networks. The theory is used to evaluate the operational performance of networks that can be modeled by probabilistic graphs. Although evaluating network reliability is an Non‐deterministic Polynomial‐time hard problem, numerous solutions have been proposed. However, most of them are based on sequential computing, which under‐utilizes the benefits of multi‐core processor architectures. This paper addresses this limitation by proposing an efficient strategy for calculating the two‐terminal (terminal‐pair) reliability of a binary‐state network that uses parallel computing. Existing methods are analyzed. Then, an efficient method for calculating terminal‐pair reliability based on logical‐probabilistic calculus is proposed. Finally, a parallel version of the proposed algorithm is developed. This is the first study to implement an algorithm for estimating terminal‐pair reliability in parallel on multi‐core processor architectures. The experimental results show that the proposed algorithm and its parallel version outperform an existing sequential algorithm in terms of execution time. Copyright © 2015 John Wiley & Sons, Ltd.     

A Bayesian‐based Reliability Estimation Approach for Corrosion Fatigue Crack Growth Utilizing the Random Walk

Structural health monitoring enables corrosion fatigue damage for in‐service structures to be evaluated and prognosis health management to perform. In this paper, a Bayesian inference method using random walks is implemented to estimate the reliability of structures such as the pipelines subjected to repeated pressurization cycles and corrosive agents. The proposed method eliminates the intermediate step in updating process and computes the cumulative distribution function instead of calculating probability density function of individual parameters in conventional ones, which is affordable for a routine program, especially convenient for practical engineering use in the field. As taking all relevant random variables into account, this approach could significantly reduce uncertainties associated. For illustration and validation purpose, both numerical and practical examples are demonstrated in details. Copyright © 2016 John Wiley & Sons, Ltd.     

Reliability Analysis Considering the Component Collision Behavior for a Large‐scale Open Source Solution

Open source software systems that serve as key components of critical infrastructures in the society are still ever‐expanding now. Many open source software systems are developed in all parts of the world, that is, Firefox, Apache HTTP server, Linux, Android, and so on. Especially, a large‐scale open source solution composed of several open source software is now attracting attention as a next‐generation software development paradigm because of the cost reduction, quick delivery, and work saving. In this paper, we propose a new approach to software reliability assessment based on stochastic differential equations and a hierarchical Bayesian model in order to consider the interesting aspect of the collision status in the binding phase of open source software. Also, we analyze actual software fault‐count data to show numerical examples of software reliability assessment considering the component collision for several open source software. Moreover, we show that the proposed reliability analysis can assist improvement of quality for the large‐scale open source solution. Copyright © 2013 John Wiley & Sons, Ltd.     

Error estimation and model adaptation for a stochastic‐deterministic coupling method based on the Arlequin framework

The paper deals with the issue of accuracy for multiscale methods applied to solve stochastic problems. It more precisely focuses on the control of a coupling, performed using the Arlequin framework, between a deterministic continuum model and a stochastic continuum one. By using residual‐type estimates and adjoint‐based techniques, a strategy for goal‐oriented error estimation is presented for this coupling and contributions of various error sources (modeling, space discretization, and Monte Carlo approximation) are assessed. Furthermore, an adaptive strategy is proposed to enhance the quality of outputs of interest obtained by the coupled stochastic‐deterministic model. Performance of the proposed approach is illustrated on 1D and 2D numerical experiments. Copyright © 2013 John Wiley & Sons, Ltd.