Integrated Decision Support System for Optimal Renewal Planning of Sewer Networks

Municipalities are under increasing pressure to adopt proactive and optimized renewal strategies to reduce the risks, life-cycle costs, and resources needed to maintain acceptable performance and service levels of their infrastructure assets. A new integrated approach for optimal renewal planning of municipal infrastructure systems has been developed. This paper discusses the application of the proposed approach to implement a GIS-based decision support system (DSS) to support the renewal planning of sewer networks. Condition rating, risk assessment, and prioritization techniques are described. A procedure for identifying and selecting the most suitable renewal technologies is also presented. A genetic algorithm-based multiobjective optimization technique is used to find a Pareto front of feasible solutions, each comprising a set of sewers to be renewed each year, along with the associated costs and expected benefits in terms of condition improvement and risk reduction. The paper also presents an example application of the prototype DSS on the sewer network in Regina, Canada.     

Prediction of Sewer Condition Grade Using Support Vector Machines

Assessing the condition of sewer networks is an important asset management approach. However, because of high inspection costs and limited budget, only a small proportion of sewer systems may be inspected. Tools are therefore required to help target inspection efforts and to extract maximum value from the condition data collected. Owing to the difficulty in modeling the complexities of sewer condition deterioration, there has been interest in the application of artificial intelligence-based techniques such as artificial neural networks to develop models that can infer an unknown structural condition based on data from sewers that have been inspected. To this end, this study investigates the use of support vector machine (SVM) models to predict the condition of sewers. The results of model testing showed that the SVM achieves good predictive performance. With access to a representative set of training data, the SVM modeling approach can therefore be used to allocate a condition grade to sewer assets with reasonable confidence and thus identify high risk sewer assets for subsequent inspection.     

Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment

Recent advances in optical sensors and computing technologies have led to the development of inspection systems for underground facilities such as water lines, sewer pipes, and telecommunication conduits. It is now possible for inspection technologies that require no human entry into underground structures to be fully automated, from data acquisition to data analysis, and eventually to condition assessment. This paper describes the development of an automated data interpretation system for sanitary sewer pipelines. The interpretation system obtains optical data from the Sewer Scanner and Evaluation Technology (SSET), which is known to be the current leading-edge technology in inspecting sanitary sewer pipelines. The proposed system utilizes artificial neural networks to recognize various types of defects in sanitary sewer pipelines. The framework of this system includes modification of digital images for preprocessing, image feature segmentation, utilization of multiple neural networks for feature pattern recognition, and the fusion of multiple neural networks via the use of fuzzy logic systems.     

Life-Cycle Cost Based Maintenance Plan for Steel Bridge Protection Systems

Life-cycle cost analysis was used to compare different alternative strategies for steel bridge paint systems. It was also used as a tool for steel bridge paint rehabilitation planning. The existing paint systems are lead-based and zinc-vinyl, while the new system is an inorganic/organic zinc, epoxy, and urethane paint system (three-coat). Economic analysis using present value (PV) and equivalent uniform annual cost (EUAC) was applied to compare several steel bridge paint system alternatives. The PV and EUAC were also used to compare different rehabilitation scenarios within the same alternative. Life-cycle cost analysis computations indicate that the three-coat paint system was better than others. Researchers concluded that the best scenario for three-cost system rehabilitation was doing spot repairs every 15 years of paint life. A maintenance plan based on life-cycle cost analysis also favored the “spot repairs every 15 years” scenario. A sensitivity analysis was also conducted to account for uncertainty in the cost, conditions, and subjective data.     

Water Distribution Network Renewal Planning

This paper provides an overview of the writers' previous work in formulating a comprehensive approach to the important problem of water distribution network renewal planning, with a particular emphasis on the computing aspects involved. As pipes in a water distribution network age in service, they are characterized by increased frequency of breakage and decreased hydraulic capacity. The resulting service failures incur utility costs for the repair or rehabilitation of the pipe systems and consumer costs for degraded system performance. The challenge to the decision maker is to determine the most cost-effective plan in terms of what pipes in the network to rehabilitate, by which rehabilitation alternative and at what time in the planning horizon, subject to the constraints of service requirements (system reliability, service pressure, etc.) A dynamic programming approach, combined with partial and implicit enumeration schemes, was used to search the vast combinatorial solution space that this problem presents. A computer program was written to implement these concepts. A hydraulic network solver is used by the program to assure the network conformance to hydraulic constraints during the search for a solution. The outcome is a strategy that identifies, for each pipe in the network, the optimal rehabilitation∕renewal alternative and its optimal time of implementation. The significance of this method is in its ability to identify an optimal rehabilitation strategy while considering the deterioration of both structural integrity and hydraulic capacity of the entire network. The best current heuristic method is limited in practical studies to a network of up to 15–20 pipe links. A more efficient heuristic method is required for implementing these principles in a larger-scale water distribution system and is the subject of current research.     

钢铁企业集成生产计划管理系统研究

针对钢铁企业生产计划管理系统与其他系统集成度不高的问题,提出了面向制造执行系统的生产计划管理系统集成解决方案;研究了生产计划管理系统和物料管理、质量管理、基础数据以及合同管理等子系统的集成关系.开发了基于B/S模式的企业生产计划管理系统,该系统的运行和实施对钢铁企业信息化具有重要的意义.     

Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines

The Federation of Canadian Municipalities reported that approximately 55% of sewer infrastructure in Canada did not meet current standards. Therefore, the burden on Canadian municipalities to maintain and prioritize sewers is increasing. One of the major challenges is to develop a framework to standardize the condition assessment procedures for sewer pipelines. Lack of detailed knowledge on the condition of sewer networks escalates vulnerability to catastrophic failures. This research presents a proactive methodology of assessing the existing condition of sewers by considering various physical, environmental, and operational influence factors. Based on historic data collected from two municipalities in Canada, structural and operational condition assessment models for sewers are developed using the multiple regression technique. The developed regression models show 82 to 86% accuracy when they are applied to the validation data set. These models are utilized to generate deterioration curves for concrete, asbestos cement, and polyvinyl chloride sewers in relation to traffic loads, bedding materials, and other pipe characteristics. The developed models are expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections, and rehabilitation requirements.     

Financial Outlay Modeling for a Local Sewer Rehabilitation Strategy

Modern infrastructure systems are highly developed, with considerable capital funding invested in them. While capital is spent on new infrastructure initiatives, the maintenance of the present infrastructure must not be neglected. Increasingly, public agencies are being urged to develop improved systematic methodology for allotting their period budgets more appropriately so that the capacity of the installed infrastructure is more fully utilized and sustained. When planning the allocation of investment funds, multiple objectives may exist that are dependent on the constraints, resources available for construction, and the interrelationships and dependencies among all of the alternatives. This makes the task of planning, prioritizing, and allocating funds a complex exercise. This paper presents a financial outlay model called PRISM, Proactive Rehabilitative Sewer Infrastructure Management, which uses linear programming to optimize allocation of funding for the local sewer network maintained by the City of Edmonton, Canada. By grouping sewer pipes into categories based on parameters of age, diameter, material type, waste types, and average depth of cover, the model provides a mechanism for determining the most appropriate allocation or appropriation of funding given a planning horizon and budgetary constraints.     

Modeling Blockage Failures in Sewer Systems to Support Maintenance Decision Making

The objective of this research is to develop and implement a stochastic method that can be applied to characterize random failures in critical infrastructure systems. We particularly focus on blockage failures in sewer systems that are nonmechanistic and result from combination of external factors, including deterioration in condition. The method was implemented using a data set consisting of sewer blockage failure records from a small municipality. Statistical tests were conducted to: (1) ensure that available data set is representative and (2) estimate parameters of distributions that appropriately characterize failure event arrival pattern. Failure trends were also analyzed to identify the influence of local factors and justify the choice of the distributions used to characterize interarrival times. Based on the analysis, we explored the challenges in developing a reliability model across the life cycle of a sewer system. In addition, specific examples were also presented to illustrate how the method can be applied to support system maintenance decisions. The results of this study illustrate how the memoryless property can be assumed in analyzing failure events, while explicitly considering context specific influences. Finally, the methods described in this paper are extensible and can be applied generally to analyzing random failures in other infrastructure systems as well.     

Modeling the Reduction in Load Capacity of Highway Bridges with Age

Bridge management systems focus on optimizing the life-cycle cost of preservation and improvement activities in a network of structures. Pontis, the predominant bridge management system currently implemented in the United States, considers load-carrying capacity as static during an incremental benefit-cost approach. Including consideration of load-carrying capacity, deterioration may enhance this model. To accomplish this goal, a method to relate load-carrying capacity to physical bridge deterioration using a combination of regression analysis and Markov chains is presented. The method is applied to historic bridge data from the United States and Hungary.     

Modeling Ventilation Phenomenon in Sanitary Sewer Systems: A System Theoretic Approach

Municipal wastewater collection systems, due to the nature of their functions, carry varying concentrations of odorous gases. The production rate and transport of these gases within and out of sewer systems depend on air flow rate in the system piping. However, municipal sewers are generally designed to only transport sewage flow without giving consideration to the air flow field. As a consequence, the movement of air into, along, and out of collection systems is for the most part uncontrolled. The purpose of this paper therefore is to provide a new design protocol based on system theoretic techniques to be used by municipal engineers and environmentalists involved in odor control and sewer foul air transport studies. The modeling formulation accounts for combined wastewater drag and pressure-induced air flows, and manhole pressurization. The developed framework is applied to both hypothetical and real sewer systems to only illustrate the applicability of the modeling formulation.     

Optimal Distribution and Control of Storage Tank to Mitigate the Impact of New Developments on Receiving Water Quality

Storage tanks are commonly installed in a combined sewer system to control the discharge of combined sewer overflows that have been identified as a leading source for receiving water pollution. The traditional approach to determine the distribution of storage tank volume in the sewer system is confined to the use of objectives within the system itself due to the limits of separate modeling of urban wastewater systems, consisting of the sewer system, wastewater-treatment plant, and receiving water. The aim of this study is to investigate the optimal distribution and control of storage tanks with an objective to mitigate the impact of new residential development on receiving water quality from an integrated modeling perspective. An integrated urban wastewater model has been used to test three optimization scenarios: optimal flow rate control, storage distribution, and a combination of these two. In addition to the cost of storage tank construction, two receiving water quality indicators, dissolved oxygen and ammonium concentration, are used as optimization objectives. Results show the benefits of direct evaluation of receiving water quality impact in the context of storage distribution optimization. Results indicate that storage allocation should be considered in conjunction with optimal flow rate control to achieve the maximum effectiveness in water pollution mitigation.     

Analysis of Life-Cycle Maintenance Strategies for Concrete Bridge Decks

This paper presents the development of a project-level decision support tool for ranking maintenance scenarios for concrete bridge decks deteriorated as a result of chloride-induced corrosion. The approach is based on a mechanistic deterioration model and a probabilistic life-cycle cost analysis. The analysis includes agency and user costs of alternative maintenance scenarios and considers uncertainties in the agency cost and the corrosion rate in the deterioration model. The tool presented in this paper can be used to find the optimal condition index of a given bridge deck that minimizes life-cycle cost. Based on the results obtained on three existing bridge decks, it is shown that the total life-cycle cost (user cost plus agency cost) is a nonlinear function of the maximum tolerable condition of the deck, Sm, and that for a practical range of Sm, the relationship between total life-cycle cost and Sm is convex.     

Integrated Storm-Water Management for Watershed Sustainability

One aspect of integrated watershed management evaluates the impact of development on the local hydrologic cycle and, in particular, drinking water, wastewater, and storm-water infrastructure. Sustainable storm-water management focuses on selecting storm-water controls based on an understanding of the problems in local receiving waters that result from runoff discharges. For example, long-term problems associated with accumulations of pollutants in water bodies include sedimentation in conveyance systems and receiving waters, nuisance algal growths, inedible fish, undrinkable water, and shifts to less sensitive aquatic organisms. Short-term problems associated with high pollutant concentrations or frequent high flows (event-related) include swimming beach closures, water quality violations, property damage from increased flooding, and habitat destruction. A wide variety of individual storm-water controls usually must be combined to form a comprehensive wet weather management strategy. Unfortunately, combinations of controls are difficult to analyze. This will require new modeling techniques that can effectively evaluate a wide variety of control practices and land uses, while at the same time ensure that the flood-control objectives also are met. The results of these new models and novel techniques used for storm-water control then can be incorporated into an evaluation of the urban water cycle for a specific service area to determine whether storm-water controls can provide additional benefits such as reduction of potable water use and reduction of sanitary sewer overflow events.     

Structural Condition Assessment of Sewer Pipelines

The need of immediate supportive measures for sustainability of municipal infrastructures calls for better understanding of the behavior of various infrastructure network systems and their components. This paper presents a study which uses artificial neural networks to investigate the importance and influence of certain characteristics of sewer pipes upon their structural performance, expressed in terms of condition rating. In this study, back propagation and probabilistic neural network (NN) models were developed and validated. The data used in the development of these models were provided by the municipality of Pierrefonds, Quebec. It comprised of parameters related to sewer pipelines, pipe diameter, buried depth/cover, bedding material, pipe material, pipeline length, age, and closed circuit television (CCTV) based structural condition rating. The first six parameters are the independent variables of the models whereas CCTV based condition rating for these pipes is the dependent variable (i.e., the output of the models). The developed NN models were used to rank the parameters, in order of their importance/influence on pipe condition. It was found that, among the studied parameters, material attributes have highest influence on pipe structural condition, respectively, followed by the geometric and physical attribute group. Sensitivity analysis was then performed to simulate the structural condition of a pipe at a range of values of each input parameters. Results of sensitivity analysis describe the nature and degree of the influence of each parameter on pipe structural condition. The developed models are expected to benefit academics and practitioners (municipal engineers, consultants, and contractors) to prioritize inspection and rehabilitation plans for existing sewer mains.     

Integration of Municipal Infrastructure Asset Management Processes: Challenges and Solutions

Municipal infrastructure management decision making is inherently an integrated process that requires the assimilation of a multitude of data, processes, and software systems. Current work practices have resulted in significant process and data fragmentation, which have subsequently created much inefficiency that impedes the implementation of effective management strategies. There is a broad consensus in the industry that adopting integrated multidisciplinary approaches is a key requirement for implementing efficient, sustainable, and proactive asset management programs. This paper discusses the main challenges for implementing integrated municipal infrastructure management environments, and proposes specific solutions to address these challenges. The proposed solutions address the systematization and coordination of work processes, the development of centralized shared data repositories based on nonproprietary integrated data models, and the organization and integration of distributed software tools into a modular and extensible enterprise-wide software environment. The implementation of a prototype sewer management environment based on the proposed solutions is also presented.     

Systems redesign in rehabilitation

A performance improvement team overhauled its facility's rehabilitation systems. A rehabilitation care plan was revised and an integrated assessment reduced 29 pages of documentation to 17 pages. Case management goals and documentation are integrated into one care plan.     

地下空间工程服役安全的认识与思考

中国的地下空间工程规模越来越大。如何使地下空间工程在开挖、建造和服役期间最大限度的保持安全和稳定,是目前乃至将来必须重视的重要课题。在分析地下空间工程面临的主要问题的基础上,提出了地下空间工程服役安全的3个关键科学问题:多场耦合作用下结构体材料损伤劣化规律;循环动载作用下结构体的动态疲劳损伤特性;支护与围岩的相互作用。总结和评述了在此方面的相关研究工作和最新进展。最后从宏观上指出了地下空间工程未来的发展趋势和需要重点关注和加强的基础性研究工作。      

Building Integrated Architecture/Engineering/Construction Systems Using Smart Objects: Methodology and Implementation

Integrated project systems hold the promise for improving the quality while reducing the time and cost of architecture/engineering/construction (AEC) projects. A fundamental requirement of such systems is to support the modeling and management of the design and construction information and to allow the exchange of such information among different project disciplines in an effective and efficient manner. This paper presents a methodology to implement integrated project systems through the use of a model-based approach that involves developing integrated “smart AEC objects.” Smart AEC objects are an evolutionary step that builds upon past research and experience in AEC product modeling, geometric modeling, intelligent CAD systems, and knowledge-based design methods. Smart objects are 3D parametric entities that combine the capability to represent various aspects of project information required to support multidisciplinary views of the objects, and the capability to encapsulate “intelligence” by representing behavioral aspects, design constraints, and life-cycle data management features into the objects. An example implementation of smart objects to support integrated design of falsework systems is presented. The paper also discusses the requirements for extending existing standard data models, specifically the Industry Foundation Classes (IFC), to support the modeling of smart AEC objects.     

Artificial Neural Network Model of Bridge Deterioration

Accurate prediction of bridge condition is essential for the planning of maintenance, repair, and rehabilitation. An examination of the assumptions (for example, maintenance independency) of the existing Markovian model reveals possible limitations in its ability to adequately model the procession of deterioration for these purposes. This study uses statistical analysis to identify significant factors influencing the deterioration and develops an application model for estimating the future condition of bridges. Based on data derived from historical maintenance and inspection of concrete decks in Wisconsin, this study identifies 11 significant factors and develops an artificial neural network (ANN) model to predict associated deterioration. An analysis of the application of ANN finds that it performs well when modeling deck deterioration in terms of pattern classification. The developed model has the capacity to accurately predict the condition of bridge decks and therefore provide pertinent information for maintenance planning and decision making at both the project level and the network level.