Condition Assessment of Buried Pipes Using Hierarchical Evidential Reasoning Model

Effective inspection and monitoring practices for the condition assessment of pipes ensure better decision(s) for repair or replacement before they fail. Pipe deterioration is a physical manifestation of the aging process in which many factors can contribute to structural failure. Various technologies/ techniques have been developed during the last few years to inspect/monitor piping systems, but how to intelligently interpret the collected data remains a challenge. In this paper, a new approach based on hierarchical evidential reasoning is proposed. This approach uses Dempster–Shafer (D-S) theory to make inferences for condition assessment of buried pipes. A hierarchical evidential reasoning model can help combine different distress indicators (bodies of evidence) at different hierarchical levels using D-S rule of combination. The proposed hierarchical evidential reasoning method is demonstrated with an example of condition assessment for a large diameter pipe. Information from multiple sources is fused to obtain a more reliable assessment of pipe deterioration.     

Dynamic Priority–Dynamic Programming Scheduling Method (DP)SM: a dynamic approach to resource constraint project scheduling

The purpose of this paper is twofold: first to introduce and evaluate a dynamic priority scheduling model developed in this research for solving the resource constraint project scheduling problem and second, to introduce an improvement made upon the first model by cross-breeding Dynamic Programming with the Dynamic Priority Scheduling Method (DPSM). The second model, called the Dynamic Priority Dynamic Programming Scheduling Method [(DP)²SM], aims at optimising the staged resource allocation decisions in DPSM. DPSM divides a project into phases (cycles), the length of which depends on the duration of the project and the period of clock cycle selected. The scheduling process starts by allocating resources to the first phase/cycle using a variety of policies, then the best schedule is selected based on an objective function. The process continues till all the activities are scheduled. In DPSM the interaction between phases is ignored while the decisions of each phase or cycle will affect all the remaining phases. Using (DP)²SM it may be possible to improve the quality of a schedule and reduce the duration of a project by optimising the overall project schedule.     

Evidential reasoning using extended fuzzy Dempster–Shafer theory for handling various facets of information deficiency

This work investigates the problem of combining deficient evidence for the purpose of quality assessment. The main focus of the work is modeling vagueness, ambiguity, and local nonspecificity in information within a unified approach. We introduce an extended fuzzy Dempster–Shafer scheme based on the simultaneous use of fuzzy interval‐grade and interval‐valued belief degree (IGIB). The latter facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge, whereas the former allows for handling the lack of information on belief degree assignments. Also, generalized fuzzy sets can be readily transformed into the proposed fuzzy IGIB structure. The reasoning for quality assessment is performed by solving nonlinear optimization problems on fuzzy Dempster–Shafer paradigm for the fuzzy IGIB structure. The application of the proposed inference method is investigated by designing a reasoning scheme for water quality monitoring and validated through the experimental data available for different sampling points in a water distribution network. © 2011 Wiley Periodicals, Inc.     

Robust decentralized multi-model adaptive template tracking

In this paper, a robust and efficient visual tracking method through the fusion of several distributed adaptive templates is proposed. It is assumed that the target object is initially localized either manually or by an object detector at the first frame. The object region is then partitioned into several non-overlapping subregions. The new location of each subregion is found by an EM¹-like gradient-based optimization algorithm. The proposed localization algorithm is capable of simultaneously optimizing several possible solutions in a probabilistic framework. Each possible solution is an initializing point for the optimization algorithm which improves the accuracy and reliability of the proposed gradient-based localization method to the local extrema. Moreover, each subregion is defined by two adaptive templates named immediate and delayed templates to solve the “drift” problem.² The immediate template is updated by short-term appearance changes whereas the delayed template models the long-term appearance variations. Therefore, the combination of short-term and long-term appearance modeling can solve the template tracking drift problem. At each tracking step, the new location of an object is estimated by fusing the tracking result of each subregion. This fusion method is based on the local and global properties of the object motion to increase the robustness of the proposed tracking method against outliers, shape variations, and scale changes. The accuracy and robustness of the proposed tracking method is verified by several experimental results. The results also show the superior efficiency of the proposed method by comparing it to several state-of-the-art trackers as well as the manually labeled “ground truth” data.     

Exploring the Relationship between Soil Properties and Deterioration of Metallic Pipes Using Predictive Data Mining Methods

Soil corrosivity is considered to be a major factor for the deterioration of metallic water mains. Using a 10-point scoring method as suggested by the American Water Works Association, soil corrosivity potential can be estimated by five soil properties: (1) resistivity; (2) pH value; (3) redox potential; (4) sulfide; and (5) percentage of clay fines. However, the relationship between soil corrosivity and pipe deterioration is often ambiguous and not well-defined. In order to identify the direct relationship between soil properties and pipe deterioration, which is defined as the ratio of the maximum pit depth to pipe age, predictive data mining approaches are investigated in this study. Both single- and multipredictor based approaches are employed to model such relationship. The advantage of combining multiple predictors is also demonstrated. Among all approaches, rotation forest achieves the best result in terms of the prediction error to estimate pipe deterioration rate. Compared to the random forest method, which is next to the best, the normalized mean square error decreased 50%. With the proposed approaches, the assessment of pipe condition can be achieved by analyzing soil properties. This study also highlights the importance for collecting more reliable soil properties data.     

EDM: Earned Duration Management,a new approach to schedule performance management and measurement

The concept of schedule monitoring and control as one of the most important functions of project and program management has not been fully exploited. One possible explanation could be the dominance of the Earned Value Management System (EVMS, also known as EVM). EVM was originally developed as a cost management and control tool which was extended to track the schedule as well. EVM and its derivatives (e.g. Earned Schedule) use cost as a proxy to measure schedule performance to control the duration of the project. While there is a correlation between schedule, cost, quality, and scope of a project, using cost to control duration has proven to be misleading. In contrast to Earned Value and Earned Schedule, the authors have developed the Earned Duration Management (EDM) in which they have decoupled schedule and cost performance measures and developed a number of indices to measure progress and performance of schedule and cost, as well as the efficacy and efficiency of the plan at any level of the project. These new indices are easy to understand, have wider applications, and can be used by contractors, clients and the scheduling offices to assess and measure schedule performance. The newly developed duration performance measures are all schedule-based and can be used for forecasting the finish date of the project.     

Deep graph transformation for attributed,directed, and signed networks

Knowledge and Information Systems - Generalized from image and language translation, the goal of graph translation or transformation is to generate a graph of the target domain on the condition of...     

Vision‐based terrain characterization and traversability assessment

This article presents novel techniques for real‐time terrain characterization and assessment of terrain traversability for a field mobile robot using a vision system and artificial neural networks. The key terrain traversability characteristics are identified as roughness, slope, discontinuity, and hardness. These characteristics are extracted from imagery data obtained from cameras mounted on the robot and are represented in a fuzzy logic framework using perceptual, linguistic fuzzy sets. The approach adopted is highly robust and tolerant to imprecision and uncertainty inherent in sensing and perception of natural environments. The four traversability characteristics are combined to form a single Fuzzy Traversability Index, which quantifies the ease‐of‐traversal of the terrain by the mobile robot. Experimental results are presented to demonstrate the capability of the proposed approach for classification of different terrain segments based on their traversability. © 2001 John Wiley & Sons, Inc.