Scheduling Inspection and Renewal of Large Infrastructure Assets

Construction diversion is necessary for damming, and First-Stage Diversion Scheme Selection (FDSS) is of significance in damming process control. To effectively select the first-stage diversion scheme, a theoretical Decision-Making Framework (DMF) is presented and its decision-making basis includes diversion risk, costs, social attitudes and duration. With multiple aspects of concerns, the FDSS can be solved by Multi-Attribute Decision Making (MADM) methods and the complex diversion risk assessment can be achieved by the Monte Carlo Simulation (MCS). Mutual Inspection (MI) is based on a MADM feature of Partially Different Results (PDR), which should involve different MADM methods in solving the same problem and can reduce method difference-derived error within MADM results by mutually contrasting the PDR and refining ranking messages. Basing on these theoretical studies, the DMF for FDSS is developed, which contains two parts, the Risk Detector that assesses diversion risk of alternatives through MCS and the Decision Solver that undertakes decision-making preliminary and MADM enhanced by MI for solving FDSS. To demonstrate the DMF with MI, a case study that applies suitable MADM methods (viz. TOPSIS and ELECTRE I) for carrying out MI is conducted. Case results show that the DMF can solve the FDSS as it selects the optimal (S6) among six alternatives, the MI can enhance the DMF as it refines ranking messages about alternatives in the final MADM results. The DMF with MI can benefit the damming industry and contribute to the overall water resources management.

Asbestos cement (AC) pipes are among the oldest assets in many water supply networks. With increasing failure rates and cost consequences, asset management tools are required to pre-empt unplanned failures and schedule future replacement at the most cost-effective time during service. This paper develops a physical probabilistic failure model for AC pipes under combined internal pressure and external loading. Uncertainty in the degradation process is accounted for using the Weibull extreme value probability distribution. Monte Carlo simulation is then used to estimate the probability of pipe failure as ageing proceeds. In the final stage of the model, a cost/benefit analysis is conducted to determine optimal scheduling of future inspection and replacement activities. The end result is a potentially useful asset management methodology in which both the probable physical lifetime and the economic lifetime of an AC pipe can be estimated.

Purpose The purpose of this paper is to review the Fuzzy Markov development for assessing the structural integrity of buried transportation bridges. In doing so, the appropriateness of Fuzzy Markov will be assessed, leading to the subsequent model. Design/methodology/approach This research will utilize the Fuzzy Markov techniques as the conceptual framework. Such methodology is further supported via the utilization and evaluation of 30 buried transportation bridges using the developed Fuzzy Markov model. Findings Subsequently, through a developed Fuzzy Markov model, this research found that as the basis of structural resilience, specific matrices for age-dependent transition probability can be compiled using conditional survival probabilities in the various structural states; as the basis of structural integrity, specific environmental and economic schemes can also be established based on inspection intervals, intervention systems and failure phases; exact inspection and maintenance intervals can be scheduled to further prolong an asset’s life; and clear and early warning signs can also be formulated for immediate intervention when the structural integrity of the asset are indeed compromised. Originality/value The gap within the literature currently surrounds the limitation of computational analysis for some buried structures such as bridges. Specifically, to streamline such evaluation and regimes, a Fuzzy Markov is developed and reviewed.

Power transformer is one of the most important and expensive equipment in a power system. Its reliability directly affects a power system. To ensure the reliable operation of a power transformer, its condition needs to be continuously monitored and evaluated. Over the past two decades, a number of diagnostic techniques have been developed for transformer condition assessment such as dissolved gas analysis (DGA), degree of polymerization (DP) measurement, polarization and depolarization current (PDC) measurement, frequency domain spectroscopy (FDS), frequency response analysis (FRA), and partial discharge (PD) detection. However, the interpretations of measurement results acquired from these diagnostics are usually based upon the empirical models, which are sometimes inaccurate and incomplete especially in abnormal transformer operation scenarios. Therefore, accurate interpreting on the measurement data obtained by the above techniques and subsequently making explicit condition assessment of transformers is still a challenge task. Nowadays, considerable efforts have been made in the field of transformer condition monitoring and assessment. Majority efforts are dedicated in developing accurate transformer models and reliable transformer fault diagnosis systems. After completing a comprehensive literature review on various diagnostic techniques for transformers condition assessment, this thesis focuses on three main aspects of transformer’s health condition, including oil characteristics and dissolved gases in transformers, moisture concentration of oil-cellulose insulation and hot spot temperature of transformer windings. Since there is a lack of common framework for applying pattern recognition algorithms (i.e. data centric approaches) to interpret oil characteristics and DGA data, this thesis firstly provides a critical review on various pattern recognition techniques for power transformer insulation diagnosis using DGA and oil characteristics datasets. A general pattern recognition application framework is then proposed. The important issues for improving the applicability of pattern recognition techniques for transformer insulation diagnosis are also discussed. To improve the data quality of training database and enhance the diagnostic accuracy of pattern recognition algorithms, a hybrid algorithm, SMOTEBoost is proposed. It adopts Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem, where samples belonging to different fault types (insulation conditions) are unevenly distributed in the training database. By using the boosting approach for reweighting and grouping data points in the training database, the SMOTEBoost can facilitate pattern recognition algorithms consistently attaining desirable diagnosis accuracies. To solve the intricate difficulties in moisture estimation of transformer oil-cellulose insulation system, this thesis introduces two modelling approaches, i.e., multi-physics finite element modelling (FEM) and particle tracing method, where the temperature dependent moisture dynamics in transformers’ insulation system is taken into account. In multi-physics approach, moisture dynamics is modelled by coupling the electromagnetic, thermal, fluid flow and moisture migration physics simultaneously. In particle tracing method, moisture diffusion is formulated from a microscopic view of water molecules’ motion. Especially, the transmission probability of water molecules (termed as particles in the paper) is employed to correlate the microscopic particles’ motion with the macroscopic moisture distribution. Extensive ageing and moisture diffusion experiments have been conducted on a prototype transformer to verify the proposed modelling approaches for an accurate estimation of moisture in transformers. This thesis also proposes a distributed parameter model to investigate the effect of moisture dynamics on dielectric response of a transformer’s cellulose insulation. The correlation between moisture distribution (under non-equilibrium conditions due to thermal transients) and dielectric response parameters (dielectric losses and permittivity) of transformer cellulose insulation is revealed. The proposed methodology can help the proper interpretation of dielectric response measurement of field transformers under thermal transients. To overcome the inaccuracy in empirical thermal dynamic models, in this thesis a moisture dependent thermal model (MDTM) is developed for estimating transformer’s hot spot temperature. In this model, nonlinear thermal resistance is formulated by considering both oil and cellulose (paper and pressboard) of the transformer. Especially, the effect of moisture concentration and hot spot temperature on the thermal resistance of cellulose is taken into account. The proposed MDTM is verified by using historical data of moisture-in-oil and temperature measurements on an in-service vegetable oil-filled transformer. To integrate every piece of data and information obtained from different transformer diagnostic measurements and subsequently evaluating the overall health condition of a transformer, this thesis proposes a data and information fusion framework based on Bayesian Network (BN). Within the Bayesian Network, Monte Carlo and Bootstrap methods are employed to extract the most informative characteristics regarding transformer condition from different diagnostic measurements. Results of case studies demonstrate that the proposed data and information fusion framework can evaluate the effectiveness of combinations of different diagnostic measurements and subsequently facilitate determining optimal diagnostic strategies involved in transformer condition assessment. It is expected that the data centric diagnostic approaches developed in this thesis can provide an accurate modelling and reliable assessment of transformer’s health condition.

Monte Carlo methods (based on iid sampling or Markov chains) for estimating integrals with respect to a proper target distribution (that is, a probability distribution) are well known in the statistics literature. If the target distribution $\pi$ happens to be improper then it is shown here that the standard time average estimator based on Markov chains with $\pi$ as its stationary distribution will converge to zero with probability 1, and hence is not appropriate. In this paper, we present some limit theorems for regenerative sequences and use these to develop some algorithms to produce strongly consistent estimators (called regeneration and ratio estimators) that work whether $\pi$ is proper or improper. These methods may be referred to as regenerative sequence Monte Carlo (RSMC) methods. The regenerative sequences include Markov chains as a special case. We also present an algorithm that uses the domination of the given target $\pi$ by a probability distribution $\pi_{0}$. Examples are given to illustrate the use and limitations of our algorithms.

Declining fish health and the occurrence of large fish kills are some of the more publicly meaningful indicators of water quality in the impaired Neuse River Estuary, North Carolina. It is generally believed that such problems are caused by the widespread depletion of dissolved oxygen—an indirect result of anthropogenic nutrient pollution. However, the development of scientific simulation models to predict how improvements in oxygen conditions will improve the health of fish and reduce the frequency of fish kills has proven elusive. As a pragmatic solution to this problem, the expert opinion of estuarine fisheries scientists in possession of relevant data and experience was elicited. The relations between joint and conditional probabilities were exploited to translate quantities that are normally hard to assess into quantities that can be drawn more directly from the experiential knowledge of the experts. A combined model of expert opinion was constructed as an influence diagram, and Monte Carlo simulation was used to generate predictions of fish health and fish kills in the Neuse River Estuary under current and improved oxygen conditions. Full model results are expressed as probability distributions, capturing the effects of natural variability and knowledge uncertainty—both contributors to total ecological risk.

A probabilistic approach is proposed to help select the most suitable nondestructive inspection (NDI) technique and associated optimal schedule for fracture-critical member/detail fatigue inspections on a specific steel bridge. The probability of detection (POD) function for the NDI technique, which is a measure of the detection accuracy, is employed. By combining probability calculations based on use of the POD function together with numerical Monte Carlo simulations of the crack propagation of the fracture-critical detail, a cost function is formulated that includes the expected cost of inspections and failure resulting from the chosen NDI technique and alternative inspection schedules. In summary, the selection of an NDI technique with an associated inspection schedule for fracture-critical inspections is formulated as an optimization problem that can guarantee minimum total cost. The inspection frequency is determined as part of the optimization that utilizes appropriate constraints on inspection intervals and a minimum acceptable (target) structural safety level. A case study for a box girder bridge is presented to demonstrate the application of the proposed probabilistic method.

Methods for estimating the failure rates of distribution reclosers, and scheduling maintenance for those reclosers based on risk to the system, are presented. Condition data that can be gathered while the recloser is in service is used to calculate a condition value. The value is compared with historical recloser data and a failure rate is estimated. The failure rates and system topology are then used to estimate the risk, a combination of reliability indices, energy not served, and cost of failure, assocated with service to each recloser. Maintenance is scheduled to minimize risk.

<p>Machine learning (ML) provides a promising paradigm for discovering and modeling structural performances and conditions through the deep mining of structural health monitoring data. This paper exhibits recent progress of ML-based structural health monitoring and condition assessment for long-span bridges. A series of novel algorithms for bridge condition assessment via correlation modeling between structural responses, computer vision-assisted structural damage detection, and data mining for wind effects are introduced. First, correlation modeling between different bridge responses is investigated by both the time series data and probability distribution, further assisting bridge condition assessment. Second, several novel CNN architectures and a few-shot meta-learning framework are also established for CV-assisted bridge damage detection. Third, wind-induced vibrations of the bridge in site are identified and modeled to predicate structural responses and evaluate operation conditions. Results show that ML techniques indeed improve the state of the art in structural health monitoring and condition assessment for long-span bridges.</p>

Development of a new fuzzy exposure model

Blackbody (BB) cavities as standard radiation sources are used in calibration for various infrared (IR) instruments in IR detection, imaging, material emissivity measuring etc. A surface pitted with cell-like mini cavities as honeycombs whose radiant characteristics is evaluated by Monte Carlo method (MCM) is applied to develop a new surface BB. To improve the precision of MCM, make it more reliable, three requisite factors are considered. First, set up correct physical model in which the radiation of a surface comes from light emitting points across the surface consistently. Second, the uniformity of random number generators are investigated, which is the precondition for the MCM simulation. Third, to perform simulating with tremendous sampling number, after all light points and their rays are tracked, the statistical results become stable, fluctuate slightly, which increase the exactitude of MCM. To implement MCM simulation, every ray of the IR lights is tracked, the position of a light point, the direction of a ray, the state that if the ray is absorbed or reflected at a reflecting point etc. are all regarded as random variables which are sampled by random number according to their probability distribution. The effective emissivity of the surface is evaluated statistically via tracking numerous rays which involves setting up the ray equation, and coordinates transformation etc. The relationships between the accuracy of computation results and simulating model, sampling method, uniformity of pseudo-random number, and sampling number have been discussed. The results show the surface with honeycombs can provide a consistent radiation near to be a diffuse emitter even the material of the surface including spectral components, and the MCM simulating results provide the criteria for surface BB design.

PurposeThe purpose of this paper is to explore how Monte Carlo Simulation (MCS) could have enhanced understanding of the risks involved in the financial strategy for revitalization of Rio de Janeiro’s central city based on the capture of value generated by government interventions.Design/methodology/approachThe study first describes the process involved in developing the financial strategy and model without MCS. Then, it shows how the MCS could have been integrated into this process and evaluates its potential impacts on the quality of risk analysis.FindingsIf MCS is fully integrated into the decision-making process, it can serve as a heuristic tool that helps team members to better understand risks by generating forecasts of land value and other variables as a probability distributions. By showing the variance of the forecasted variable, MCS integrates elements of modern risk analysis into financial model development in a cost-effective manner.Research limitations/implicationsMCS covers only the risks associated with the variables in the financial model. Events that seem extremely unlikely (i.e. “black swans”) can occur and must be assessed separately.Practical implicationsMCS can help analysts to understand the financial risks of large-scale development projects involving value capture, even in the prefeasibility stage.Social implicationsBy facilitating value capture, MCS could help close the financing gap for sustainable urban development and subsidies for lower income families.Originality/valueThe study “retrofits” MCS on a successfully completed financial prefeasibility study to assess its usefulness as a heuristic tool.

Monte Carlo simulation (MCS) has been widely used for the uncertainty propagations of building simulation tools. In general, most unknown inputs for the MCS are regarded as single probability distributions based on experts’ subjective judgements and assumptions, when simulation information and measured data are inaccurate and insufficient. However, this can lead to meaningless and untrustworthy results, since the results are obtained using only single probability distributions without considering reducible possibilities of some unknown inputs. This paper introduces a fuzzy MCS for dealing with the aforementioned problems. In comparison with the MCS, the fuzzy MCS has the advantage of considering the aleatory and epistemic uncertainty, and can provide a family of probability distributions. This paper also discusses how fuzzy MCS could be effectively used for uncertainty and global sensitivity analysis.

This paper presents a novel Monte Carlo method (WeLMoS, Weighted Likelihood Monte-Carlo sampling method) that has been developed to perform Bayesian analyses of monitoring data. The WeLMoS method randomly samples parameters from continuous prior probability distributions and then weights each vector by its likelihood (i.e. its goodness of fit to the measurement data). Furthermore, in order to quality assure the method, and assess its strengths and weaknesses, a second method (MCMC, Markov chain Monte Carlo) has also been developed. The MCMC method uses the Metropolis algorithm to sample directly from the posterior distribution of parameters. The methods are evaluated and compared using an artificially generated case involving an exposure to a plutonium nitrate aerosol. In addition to calculating the uncertainty on internal dose, the methods can also calculate the probability distribution of model parameter values given the observed data. In other words, the techniques provide a powerful tool to obtain the estimates of parameter values that best fit the data and the associated uncertainty on these estimates. Current applications of the methodology, including the determination of lung solubility parameters, from volunteer and cohort data, are also discussed.

Modeling data envelopment analysis by chance method in hybrid uncertain environments