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Water scarcity as well as social and economic damages caused by the increasing amounts of non-revenue water in the water distribution networks (WDNs) have been prompting innovative solutions. A great deal of potable water is wasted due to leakage in the WDNs all over the world. Hence, various leak detection approaches have been explored, including the promising application of acoustic devices. Exploiting the benefits of technological advances in acoustic devices, signal processing, and machine learning (ML), this study aimed to develop a sophisticated system for leak detection in WDNs. Different from laboratory-based studies, this study was conducted on real WDNs in Hong Kong and lasted for about two years. Utilizing acoustic emissions acquired using wireless noise loggers, various ML algorithms were explored to develop inspection models for in-service and buried WDNs. ML classification algorithms can identify patterns in the acquired signals for leak and no-leak statuses. Thus, a combination of features describing acoustic signals in time and frequency domains was utilized to facilitate the development of ML models. Separately for metal and non-metal WDNs, ten well-known ML algorithms were used to develop leak detection models. The validation results demonstrate the promising application of noise loggers and ML for leak detection in real WDNs. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Learning (DL) leak detection models demonstrated a largely stable performance and a very good accuracy, particularly for new unlabelled cases. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) methods are typically utilized to thoroughly inspect existing defects in the concrete bridge deck and tackle the limitations of common inspection practices (e.g. visual inspection). Nevertheless, the reliability of inspection outcomes crucially depends on choosing the most appropriate NDT technologies. In this regard, a comprehensive Performance Assessment Model (PAM) was developed. The developed model incorporated 40 parameters to precisely assess the performance of different NDT technologies from diverse perspectives (e.g. defect detection capability, ease of use, speed, and cost). The required data were collected through a survey questionnaire. The model utilized the Analytic Network Process (ANP) technique to calculate the importance weight of each parameter, whereas the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was exploited to compute the performance index (PI) of NDT technologies. The outcomes of this study clearly illustrate the disparity in the performance of different NDT technologies. Furthermore, it was shown that none of these technologies could either exhibit the best performance in all the proposed parameters or efficiently identify all types of defects. Based on the PAM results, a selection model was proposed to assist bridge authorities and consultants in choosing the most efficient NDT technologies for inspection purposes. [ABSTRACT FROM AUTHOR]

Condition assessment of aging infrastructure is a growing concern all over the world. A systematic performance assessment is a pre-requisite for effective asset and infrastructure management. Accordingly, this article develops an integrated approach for the performance assessment of the municipal infrastructure. The main objectives are to develop an approach to assess, measure, and combine the performance levels of water, sewer, and road infrastructure and then integrate those values into a performance index. The methodology involves developing assessment criteria from literature and using interviews with professionals. A series of workshops was conducted with professionals in Canada. Throughout the workshops, questionnaires were collected based on the analytic hierarchy process (AHP) technique. Using AHP and fuzzy set theory, data was aggregated, and the results were developed. The analysis proved that the road roughness rating has the highest impact on the performance level followed by the number of breaks that occurred within the water network. Furthermore, the developed integration model was validated by professional engineers working in the field and has shown potential in terms of providing a reliable assessment of the overall state of the infrastructure. [ABSTRACT FROM AUTHOR]

Bridges are aging and deteriorating, thus, reliable deterioration modeling is regarded as one of the vital components of Bridge management systems. This article presents an automated defect-based tool to predict the future condition of the bridge decks by calibrating the Markovian model based on a hybrid Bayesian-optimization approach. The in-state probabilities are demonstrated in the form of posterior distributions, whereas the transition from a condition state to the next lower state is a function of the severities of five types of bridge defects. In the present study, the Bayesian belief network is employed to construct the likelihood function by modeling the dependencies between the bridge defects. The maximum entropy optimization is incorporated to compute the missing conditional probabilities. The proposed approach utilizes Markov chain Monte Carlo Metropolis-Hastings algorithm to derive the posterior distributions. Finally, a stochastic optimization model is designed to build a variable transition probability matrix for each five-year zone via genetic algorithm. An automated tool is programmed using C#.net programming language to facilitate the implementation of the developed deterioration model by the users. Results show that the proposed model outperformed some commonly-utilized deterioration models as per three performance indicators which are: root-mean squared error, mean absolute error, chi-squared statistic. [ABSTRACT FROM AUTHOR]

Water pipelines deteriorate overtime due to several distressing factors. To keep water pipelines in good condition, municipalities need to use reliable and credible deterioration models and inspection plans to better manage their rehabilitation and maintenance. Thus, this paper presents the development of deterioration models and patterns of water pipelines. The deterioration models consider different water pipe sizes and materials as well as different surrounding environmental conditions which affect their deterioration rates. As a prerequisite to the development of such deterioration models, a condition assessment model for water pipelines was first developed. Questionnaires were distributed among experts to determine the weights of the factors affecting water pipeline conditions using the fuzzy analytic network process. Monte-Carlo simulation was used to account for the large uncertainties of the calculated weights in the development of the condition assessment model. The validation of the model, which was performed using historical data, yielded an average validity percentage of 93.59%. The developed models are expected to help municipalities and decision makers to accurately plan for future water pipelines maintenance and rehabilitation activities based on their different deterioration patterns. It takes into consideration both the uncertainties at the initial stage and those accumulated during the calculation process. [ABSTRACT FROM AUTHOR]

Estimating the costs of failure for sewer pipelines is usually accompanied with uncertainties because of the difficulty in capturing the relationship between the physical and economical characteristics of failed pipelines. To reduce such uncertainties economic loss models are usually used to evaluate the consequences of failure. This paper presents a methodology to estimate economic loss as a result of sewer pipelines’ failure using cost benefit analysis approach. Costs of sewer pipelines’ failure in addition to costs resulting from avoiding such failures are identified and analysed. To validate the proposed methodology, actual costs from a real failure incident were compared with the proposed model outputs. The model could estimate the direct and indirect costs with a deviation ranging between 10-12% and 22-30%, respectively. By implementing the proposed methodology on two case studies, it was found that the indirect costs as a result of sewer pipelines’ failure represent a significant portion ranging between 89 and 94% of the total costs of failure. Also, it was found that costs related to environment, delays to work and traffic disruptions contribute by 12-35% to the indirect costs. [ABSTRACT FROM AUTHOR]

A condition assessment model for gravity and pressurised sewer pipelines using Fuzzy Set Theory (FST), and Evidential Reasoning (ER) with the aid of Fuzzy Analytical Network Process (FANP) integrated with Monte-Carlo Simulation is presented in this paper. Seventeen factors were considered for gravity pipelines in addition to the operating pressure for pressurised pipelines. The model was developed using relative weights for the different factors affecting pipelines condition which were obtained using FANP integrated with Monte-Carlo Simulation based on the results of a questionnaire that was distributed to experts working in the field of infrastructures. FST was used to set thresholds for the different effect values of factors on the pipelines' condition, whereas ER was used to determine the final condition assessment index for the pipeline by aggregating both the relative weights and effect values for the different affecting factors. [ABSTRACT FROM AUTHOR]

The American infrastructure report card in 2013 rated the US water system infrastructure with grade of ‘D’. The Canadian infrastructure report card in 2012 stated that around 15.4% of Canada’s water infrastructure has a condition of fair to very poor. Thus, there is a critical need to develop efficient inspection, maintenance and rehabilitation plans for water distribution networks. However, such plans require an assessment tool to evaluate the performance and condition of water distribution networks. Therefore, the main purpose of this paper is to develop an integrated performance assessment model for water distribution networks. Two modules were developed to assess the performance of water pipelines and accessories, respectively. A third module was developed to assess the performance of water segments that includes pipelines and accessories. Moreover, a fourth module was built to assess the performance of each sub-network and the entire network based on the segments’ connection type using a reliability-based approach. To assess the performance of the water distribution network, the critical factors affecting its pipelines and accessories were identified and studied. The fuzzy analytic network process technique was used to obtain the importance weights of the identified factors. [ABSTRACT FROM AUTHOR]

The 2013 report card of America's infrastructure has scored the condition of oil and gas pipelines as D+ which means that such pipelines are in a relatively poor condition. More than 10,000 failures have been recorded in the US. These failures have resulted in environmental, health and property damages. Therefore, there is a definite need to give more attention to the maintenance of oil and gas pipelines. This paper develops a comprehensive model for the maintenance planning of oil and gas pipelines. The model selects rehabilitation/repair alternatives for oil and gas pipelines based on their condition during their service life. These alternatives are then used to calculate the cash flow throughout the service life of these infrastructures. The model, which uses Monte Carlo simulation and fuzzy approach to address the uncertainties in the estimation of the maintenance operation costs and the economic parameters, calculates the Equivalent Uniform Annual Worth of the identified alternatives. The optimum maintenance programmes consist of the alternatives that have the lowest life cycle cost of oil and gas pipelines. The model is expected to support pipeline operators in the maintenance decision-making process of oil and gas pipelines. [ABSTRACT FROM PUBLISHER]

A new multi-objective decision support system (MODSS) is developed for rehabilitation planning of public infrastructures. The method is generic and provides decision-makers a set of optimal rehabilitation tradeoffs over a desired analysis period. Two main objective functions are handled simultaneously, namely cost and performance, in addition to a set of bounding constraints. The method is based on a newfitness-orientedtechnique where problem knowledge is taken into account. In order to analyse cost and performance together, a normalisation technique of both objectives is achieved through an innovative time-value concept for both cost and condition states. The proposed method is based on life-cycle costing (LCC) methodology using a dynamic Markov chain to represent the deterioration mechanism and genetic algorithm is used to find the optimal rehabilitation profile. A case study is presented with a comparison between the traditional Markov decision process (MDP) and the newly developed method. The MODSS results in a lower LCC and is found practical in providing a complete maintenance and rehabilitation plan over a required study period. It is proven that the developed multi-objective optimisation is an effective tool in analysing real-life situations involving conflicting goals. Also, weighted sum method could be easily implemented and its outcome is sufficient given that many external factors might alter the decision-makers choice irrespectively of the optimisation method that is used. Furthermore, genetic algorithm is proven useful in the optimisation process in overcoming the computational difficulties associated with large combinatorial problems. The new method is beneficial to researchers and practitioners as it provides a major step towards a broad infrastructure management system. [ABSTRACT FROM PUBLISHER]

Wastewater treatment plants (WTPs) are among the most complex municipal infrastructure systems that serve large populations. Unfortunately, many studies have shown that the WTPs, in the USA and Canada, are facing unprecedented deterioration due to ageing and improper maintenance plans. This situation is aggravated by the lack of adequate funds for upgrading and maintenance. In 2008, Statistics Canada estimated that WTPs exceeded 63% of their useful lives, the highest level among public infrastructure facilities. Similarly, the WTP performance in the USA had a near-failure average grade ofD − . These facts show the urgent need for rehabilitation decision tools to keep these facilities running effectively. This research aims to respond to such a pressing need by developing a condition-rating index (CRI) model for the WTP infrastructure. The CRI is developed using an integrated approach of the analytical hierarchy process with the multi-attribute utility theory. The required data for these models are collected via questionnaires from site visits and interviews with experts in Canada and the USA. The results reveal that physical factors have the highest impact on deterioration of WTP infrastructure and that pumps are the most vulnerable infrastructure unit. The developed CRI workability is proved using data of three WTPs from Canada and the USA, which show robust results. [ABSTRACT FROM PUBLISHER]

Even though they are safe and economical transportation means of gas and oil products around the world, pipelines can be subject to failure and degradation generating hazardous consequences and irreparable environmental damages. Therefore, gas and oil pipelines need to be effectively monitored and assessed for optimal and safe operation. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models used corrosion features as the sole factor to assess the condition of pipelines. Therefore, the objective of this paper was to develop a condition assessment model of oil and gas pipelines that considers several factors besides corrosion. The proposed model, which uses both analytic network process and Monte Carlo simulation, considers the uncertainty of the factors affecting pipeline condition and the interdependency relationships between them. The performance of the model was tested on an existing offshore gas pipeline in Qatar and was found to be satisfactory. The model will help pipeline operators to assess the condition of oil and gas pipelines and hence prioritise their inspections and rehabilitation requirements. [ABSTRACT FROM AUTHOR]

Oil and gas pipelines transport millions of dollars of goods everyday worldwide. Even though they are the safest way to transport petroleum products, pipelines do still fail generating hazardous consequences and irreparable environmental damages. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models were limited to one failure type, such as corrosion failure, or relied mainly on expert opinion analysis. The objective of this paper is to develop a model that predicts the failure cause of oil pipelines based on factors other than corrosion. Two models are developed to help decision makers predict failure occurrence. Regression analysis and artificial neural networks (ANNs) models were developed based on historical data of pipeline accidents. The two models were able to satisfactory predict pipeline failures due to mechanical, operational, corrosion, third party and natural hazards with an average validity of 90% for the regression model and 92% for the ANN model. The developed models assist decision makers and pipeline operators to predict the expected failure cause(s) and to take the necessary actions to avoid them. [ABSTRACT FROM AUTHOR]

Poorly maintained pavement marking might certainly contribute to road accidents. The cost of road accidents is estimated to be 10–25 billion Canadian dollars annually. Accordingly, it is necessary for municipalities to develop a strategic cost-effective plan in order to renew and restripe pavement markings. Therefore, the objective of the present research is to model the effect of various factors on pavement marking conditions. Data on Alkyd paint pavement marking material are collected from the city of Ottawa, Ontario, Canada. Since the collected data from municipalities in Canada always include input variables and fail to provide output variable(s) (e.g. condition), an unsupervised neural network (UNN) model is first developed to generate the condition of pavement marking (output). Then, regression and neurofuzzy models are developed based upon the results of UNN model. The developed models are validated in which they show satisfactory results. A sensitivity analysis is performed to show the effect of changing the input variables on the developed models’ output(s). [ABSTRACT FROM AUTHOR]

Life cycle cost (LCC) is an essential approach to decide on alternative rehabilitation strategies for infrastructure systems, such as water mains. The research presented in this article identifies several rehabilitation methods for water mains, which are classified into three main categories: repair, renovation and replacement. A simulation-based LCC (SLCC) model is developed to compare different rehabilitation scenarios/alternatives for various types of water mains (i.e. cast iron, ductile iron, concrete, polyvinyl chloride and asbestos cement). Results show that ‘open trench’ and ‘slip lining’ are the most appropriate methods for the ‘repair’ and ‘renovation’ categories, respectively. However, the most suitable method for the ‘replacement’ category is ‘pipe bursting’ for pipe diameters less than 750 mm (<30″) and ‘open cut’ for pipe diameters greater than 750 mm (>30″). A rehabilitation plan is developed based on the SLCC results. This plan recommends repairing pipes using ‘open trench’ until the breakage rate reaches 0.5 breaks/km/year, then, replace the pipe beyond this threshold. Based on the designed SLCC model, web-based software is developed to determine the optimal rehabilitation scenarios. The developed model and software help academics and practitioners (e.g. municipal engineers) to predict the suitable new installation and/or rehabilitation programs as well as their corresponding costs. [ABSTRACT FROM PUBLISHER]

The ability to regularly deliver safe drinking water is a constant challenge to municipalities worldwide. In Canada, the replacement/rehabilitation cost of water mains is estimated to be $28 billion (1997–2012). Therefore, selecting cost-effective repair and/or rehabilitation scenario(s) is essential to optimise the quality of existing water mains and minimise unnecessary rehabilitation costs. The research presented in this paper identifies several rehabilitation methods for water mains, which are classified into three main categories: (1) repair (i.e. open trench, sleeves); (2) renovation (i.e. slip lining, cement mortar lining, epoxy lining, cured in place pipe (CIPP)); and (3) replacement (i.e. pipe bursting, micro-tunnelling, horizontal directional drilling, auger boring, open cut). Due to complexity, scarcity, and enormity of data required to perform life cycle cost (LCC) and select the cost-effective scenario(s), the research presented focuses on LCC data acquisition and analysis. Data were collected from contractors and municipalities in Canada. Rehabilitation decision trees were developed as a preparation step for future LCC implementation. Breakage rate analysis was successfully developed to predict the intervals of various rehabilitation alternatives. The research presented is relevant to researchers and practitioners (municipal engineers, consultants, and contractors) to prioritise pipe inspection and rehabilitation planning for existing water mains. [ABSTRACT FROM AUTHOR]

Inadequate and poorly maintained pavement markings are considered to be one of the largest contributing factors to fatal motor vehicle crashes. As a result, it is essential to apply the appropriate pavement marking material for all weather conditions in order to increase public safety and reduce motor vehicle crashes. Building a strategic plan to renew and re-stripe pavement marking is receiving increasing interest from companies/authorities that manage the pavement marking in order to reach the most cost-efficient management plan of the available pavement marking materials. The objective of this paper is to develop pavement marking performance models that predict the condition of different marking materials under various service conditions including weather, traffic and snow removal plans. The developed models are validated and the results show that the average percent validity varies from 87% to 99%. Marking performance is assessed using a condition rating scale, which numerically ranges from 1 to 5 and linguistically from excellent to critical, respectively. Deterioration curves are developed that assess the condition of the pavement marking based on the developed models. They are expected to benefit academics and practitioners (municipal engineers, consultants, and contractors) to prioritise inspection, stripping, and re-stripping planning for various pavement markings. [ABSTRACT FROM AUTHOR]

The deteriorating condition of water mains in Canada and US calls for rehabilitation strategies that accounts mainly for budget and level of service constraints. These water mains have received ‘D’ grade in the two countries. Decision support models can assist decision makers regarding when to rehabilitate and whether to repair, renovate or replace section(s) of water mains. The literature indicates that decision models should account for life cycle cost, uncertainty, long-term planning, targeted levels of service and budget constraints. The objectives of this paper are to: identify and group rehabilitation methods, present decision support model to rank and select most suitable rehabilitation method(s), and study the impact of rehabilitation methods on the functional and structural performance of water mains. The developed decision support model accounts for life cycle cost of each competing scenario along with the associated uncertainty. The model, unlike available models, can effectively account for vagueness, qualitative assessments and human judgment associated with input data. A case study of a water main network was analysed in order to demonstrate the use of the developed model and to illustrate its essential features. The results obtained indicate that the model can support the generation of well-informed decisions in a timely manner. [ABSTRACT FROM PUBLISHER]

Continuous flight auger (CFA) piles are widely used in the US and the UK as well as in many other countries. This paper focuses on developing simulation models to provide productivity and cost for various pile sizes and depths. Two simulation models were developed. Based on these models, various charts were developed to assess productivity and cost of the CFA process. Results showed that productivity is approximately 14, 24 and 93 piles (holes)/day for 0.36 m diameter piles at 21.34 m, 12.19 m, and 3.05 depths, respectively. In addition, a pile of 0.36 m diameter and 12.19 m depth costs approximately US$790.00; however, it costs approximately US$1200/pile for 21.34 m depth. For the same pile size, the cost per unit volume is approximately US$620.00/m³. The developed tools play an essential role in the CFA decision-making process. The results of simulation models are validated with an average validation factor (VF) of 1.01. This paper provides charts for practitioners' usage to schedule and price CFA pile construction projects. In addition, it provides researchers with a methodology for applying simulation to the CFA pile construction process and its limitations. [ABSTRACT FROM AUTHOR]

The assessment process of productivity and cost of bored pile construction is dictated by unseen subsurface obstacles, lack of contractor experience and site planning. These problems complicate the estimator's role in evaluating pile equipment productivity and cost. Current research discusses the assessment of piling process productivity and cost using the deterministic technique. Data are collected through questionnaires, site interviews and telephone calls to experts in various construction companies. Many variables have been considered in the piling construction process, such as pile size, depth, pouring method, soil type and construction method. Five deterministic models have been designated to assess productivity, cycle time and cost. The developed models are validated whereas 79% of the outputs have been predicted with more than 75% accuracy. Consequently, three sets of charts have been developed to provide the decision-maker with a solid planning, scheduling and control tool for piling projects. If a pile has 60' depth with f-18 (18? diameter pile) in clay soil using a 5' auger height, the cycle time is estimated as 56 and 65.5 minutes; however, productivity is 6 and 5 holes/day for dry and wet methods, respectively. [ABSTRACT FROM AUTHOR]