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This paper implemented an optimal design of non-conventional multiple tuned mass damper (NC-MTMD) system for coal scuttles in power plant. The NC-MTMD was introduced using base isolation for the coal scuttles. Mathematical models of the NC-MTMD were derived by considering multiple three-dimensional (3D) modes. Base seismic excitation is represented by 3D Kanai-Tajimi filtered white noise. Properties of the base isolation (stiffness, damping) were calibrated through global optimisation using base shear and torsion as the objective function. Derivative (e.g. Global Search, Multi Start), or derivative free (e.g. Pattern Search, Genetic Algorithm and Simulated Annealing) algorithms were considered. Pattern Search algorithm showed better convergence, efficiency and stability performance. A comparative study was performed for single- and multi-mode cases. Robustness of the optimum design was investigated quantitatively. Design parameter variation and mass variation (due to different levels of coal storages) were considered and their influences were studied. [ABSTRACT FROM AUTHOR]

Natural hazard triggering technological disasters (Natech) events pose risks to industrial facilities and process plants. As these plants handle hazardous materials, they can endanger nearby residential areas and have financial consequences. Thus, proper Natech risk assessment is required for effective prevention, mitigation and emergency response planning at industrial plants and nearby residential areas. The parameters used to quantify Natech risk assessment are subject to uncertainties and their interactions are non-linear. In this study, a Bayesian belief network-based Natech risk assessment model is developed to assess the earthquake-related Natech risk considering different levels of uncertainties. The cause and effect relationships between different parameters are constructed based on published body of knowledge and expert knowledge. The capabilities of the proposed model are demonstrated for the earthquake-related Natech risk assessment of Kobe City, Higashinada Ward, Japan because of the Great Hanshin earthquake in 1995. The proposed model is also capable of performing both predictive analysis and diagnostic analysis. [ABSTRACT FROM AUTHOR]

Oil and gas (O&G) pipelines are expensive assets that cross through both the ecologically sensitive and densely populated urban areas. The pipeline failure may have potentially significant consequences for both the natural and human environments. In order to maintain the integrity of O&G pipelines, inspection and maintenance processes should be governed by efficient policies. The objective of this paper is to conduct a state-of-the-art review of maintenance policies of O&G pipelines to investigate their advantages, limitations, and associated implementation issues. Maintenance policies can be categorised into corrective, preventive, predictive and proactive. Corrective maintenance policies (1940s) were based on the ‘repair when broke’ philosophy. Economic considerations shifted practice towards preventive maintenance (1970s to 1990s); later with improved inspection techniques and environmental regulations, predictive and proactive or risk-based maintenance (RBM) policies were developed. This review explicates different methodologies for RBM and related issues, e.g. uncertainties and variability, conservative assumptions, etc. Uncertainties associated with investigation and prediction of defects have been more frequently reported in the literature so far. Moreover, existing studies primarily focused on reducing the likelihood and cost of failure, whereas consideration of environmental factors in overall risk has been a relatively less addressed issue. [ABSTRACT FROM AUTHOR]

The current performance-based building design considers maximum interstorey drift (MISD) ratio as the main structural performance indicator. Observations from past earthquake and reported studies, however, have highlighted that residual interstory drift (RISD) ratio has become an important factor in assessing post-earthquake safety of buildings, and decision in economic feasibility of repair and reconstruction. Improving post-earthquake performance evaluation of buildings enables decision-makers prioritise repair and tag high-risk buildings. The MISD and RISD are subject to uncertainties and have non-linear relation with the input parameters. Thus, in this paper, analytical surrogate model of MISD and RISD ratios are developed using Gaussian process (GP). To show utility of the GP model, a new hybrid building system, cross laminated timber (CLT)–steel moment resisting frame hybrid system, was considered. The hybrid building was design for the seismicity of Vancouver, BC, and meets the current steel design code. For the GP surrogate model, the hybrid building input parameters considered were: infill pattern of the CLT, bracket spacing of the connection between the CLT and steel frame and panel thickness and strength of the CLT. In addition, sensitivity of four ground motion indicators was considered as surrogate input into the GP model: peak ground acceleration, ratio of peak ground acceleration/peak ground velocity, Arias intensity measure and significant duration. In general, the GP model showed good predictive performance of MISD and RISD ratios. In particular, the best predictions were obtained using the ratio of peak ground acceleration/peak ground velocity as a covariate. [ABSTRACT FROM AUTHOR]

Safety assessment of oil and gas (O&G) pipelines is necessary to prevent unwanted events that may cause catastrophic accidents and heavy financial losses. This study develops a safety assessment model for O&G pipeline failure by incorporating fuzzy logic into Bayesian belief network. Proposed fuzzy Bayesian belief network (FBBN) model explicitly represents dependencies of events, updating probabilities and representation of uncertain knowledge (such as randomness, vagueness and ignorance). The study highlights the utility of FBBN in safety analysis of O&G pipeline because of its flexible structure, allowing it to fit a wide variety of accident scenarios. The sensitivity analysis of the proposed model indicates that construction defect, overload, mechanical damage, bad installation and quality of worker are the most significant causes for the O&G pipeline failures. The research results can help owners of transmission and distribution pipeline companies and professionals to prepare preventive safety measures and allocate proper resources. [ABSTRACT FROM PUBLISHER]

This paper presents a novel decision-making framework for seismic vulnerability assessment of bulk power systems by using hierarchical network decomposition. This decomposition incorporates both the electrical properties of the network (e.g. electrical distance, power flow) and the system's configuration (i.e.form). Vulnerability is evaluated in terms of two basic criteria: form and strength. Thus, the hierarchical decomposition is used to evaluate weaknesses in the intrinsic structure of the system and to identify critical elements in terms of form. On the other hand, the element mechanical properties characterise the strength, which is defined in terms of the probability of obtaining a certain damage level of the power system under different earthquake scenarios. The proposed approach is illustrated with an assessment of the benchmark case of the IEEE 118 Bus Test Case. Results showed it provides a better insight and understanding of seismic vulnerability compared to traditional methods. [ABSTRACT FROM PUBLISHER]

Large-magnitude earthquakes can damage high-voltage transformers, trigger power flow disruption and impact the economy and society. However, methods that enable large transformer vulnerability assessment in a practical and rigorous way are scarce. This paper proposes a probabilistic framework using Bayesian belief networks (BBNs) to predict the damage of high-voltage transformers subjected to seismic events. This framework incorporates major causes of transformer vulnerability at once, such as liquefaction, rocking response of the transformer, and interactions with interconnected equipment, which are otherwise commonly studied in isolation. To demonstrate the application of the framework, the paper elaborates on each step of the BBN framework, which is then validated with historical empirical data. Furthermore, the value of the proposed method is illustrated with high-voltage transformers in substations of the electric value BC Hydro in British Columbia, Canada. The paper also offers a sensitivity analysis that evaluates the effects of input variables on transformer damage. The proposed framework is simple to perform in practice, and the results are expected to support decisions on mitigation measures, seismic risk management, and to provide a step towards modelling the vulnerability of entire electrical substations. [ABSTRACT FROM AUTHOR]

In infrastructure management, multi-criteria decision-making (MCDM) has emerged as a decision support tool to integrate various technical information and stakeholder values. Different MCDM techniques and tools have been developed. This paper presents a comprehensive review on the application of MCDM literature in the field of infrastructure management. Approximately 300 published papers were identified that report MCDM applications in the field of infrastructure management during 1980–2012. The reviewed papers are classified into application to the type of infrastructure (e.g. bridges and pipes), and prevalent decision or intervention (e.g. repair and rehabilitate). In addition, the papers were also classified according to MCDM methods used in the analysis. The paper provides taxonomy of those articles and identifies trends and new developments in MCDM methods. The results suggest that there is a significant growth in MCDM applications in infrastructure management applications of MCDM over the last decade. It has also been noted that many decision support tools based on multiple MCDM methods have been successfully used for infrastructure management. [ABSTRACT FROM PUBLISHER]

Infrastructure risk management practices enable decision-makers to effectively monitor and assess structural condition for repairing/replacing elements before major damage or collapse state is reached. Improved techniques have enhanced inspection and monitoring of infrastructure, but assessment and interpretation of the collected data remains a challenge. In this article, a hierarchical evidential reasoning (HER) framework is proposed for the condition assessment of bridges. The approach involves using a HER framework for classifying bridge data into primary, secondary, tertiary and life safety-critical elements. The proposed HER framework combines different distress indicators (bodies of evidence) at different hierarchical levels. The information is aggregated using Dempster–Shafer (D–S) and Yager rule of combination to propagate both aleatory and epistemic uncertainties throughout the model. Furthermore, importance and reliability factors (collectively termed ‘‘credibility factor’) are introduced for discounting evidence based on importance of bridge element and reliability of the collected data. The data are systematically combined to obtain primary/secondary/tertiary/life safety-critical condition indices. Finally, an overall bridge condition index is obtained. The indices are based on information from multiple sources thereby providing a more reliable assessment of bridge condition. The HER framework is applied to data from an existing bridge in order to demonstrate application of the proposed approach. [ABSTRACT FROM AUTHOR]