Synthetic rating system for railway bridge management

Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc will reduce the remaining life of bridges. Bridges are currently rated individually for maintenance and repair actions according to the structural conditions of their elements. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical rating methods are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system which will be capable of rating a network of railway bridges. This paper introduces a new method for rating a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this research is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation between them

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method

Serviceability of large-Scale systems

One of the most important research fields of network sciences is the robustness of networks. A recently answered important question was the following: Which network topologies are more resistant to random malfunctions and/or direct attacks? Nevertheless, until now, which system topology can be maintained and how to manage maintenance more efficiently and effectively have been open questions. However, these questions are the keys both to designing large-scale systems and to scheduling maintenance tasks. This paper proposes a new means to analyze the maintainability of a large system by combining two kinds of networks, i.e., the reliability diagram of the system (1) and the network of scheduled maintenance tasks (2). This paper shows how to assign maintenance task(s) to a system component to increase the reliability of the system. With the proposed method, the maintainability of large-scale systems can be analyzed

State-of-research on performance indicators for bridge quality control and management

The present study provides a review of the most diffused technical and non-technical performance indicators adopted worldwide by infrastructure owners. This work, developed within the European COST Action TU 1406—“Quality specifications for roadway bridges, standardization at a European level,” aims to summarize the state-of-art maintenance scheduling practices adopted by bridge owners, mainly focusing on the identification and classification of the most diffused performance indicators (PIs). PIs are subdivided in technical and non-technical ones: for this latter subclass, PIs are classified in environmental, social and economic-targeted. The study aims to be a reference for researchers dealing with performance-based assessments and bridge maintenance and management practices.Peer ReviewedPostprint (published version

Condition Assessment of Concrete Bridge Decks Using Ground and Airborne Infrared Thermography

Applications of nondestructive testing (NDT) technologies have shown promise in assessing the condition of existing concrete bridges. Infrared thermography (IRT) has gradually gained wider acceptance as a NDT and evaluation tool in the civil engineering field. The high capability of IRT in detecting subsurface delamination, commercial availability of infrared cameras, lower cost compared with other technologies, speed of data collection, and remote sensing are some of the expected benefits of applying this technique in bridge deck inspection practices. The research conducted in this thesis aims at developing a rational condition assessment system for concrete bridge decks based on IRT technology, and automating its analysis process in order to add this invaluable technique to the bridge inspector’s tool box. Ground penetrating radar (GPR) has also been vastly recognized as a NDT technique capable of evaluating the potential of active corrosion. Therefore, integrating IRT and GPR results in this research provides more precise assessments of bridge deck conditions. In addition, the research aims to establish a unique link between NDT technologies and inspector findings by developing a novel bridge deck condition rating index (BDCI). The proposed procedure captures the integrated results of IRT and GPR techniques, along with visual inspection judgements, thus overcoming the inherent scientific uncertainties of this process. Finally, the research aims to explore the potential application of unmanned aerial vehicle (UAV) infrared thermography for detecting hidden defects in concrete bridge decks. The NDT work in this thesis was conducted on full-scale deteriorated reinforced concrete bridge decks located in Montreal, Quebec and London, Ontario. The proposed models have been validated through various case studies. IRT, either from the ground or by utilizing a UAV with high-resolution thermal infrared imagery, was found to be an appropriate technology for inspecting and precisely detecting subsurface anomalies in concrete bridge decks. The proposed analysis produced thermal mosaic maps from the individual IR images. The k-means clustering classification technique was utilized to segment the mosaics and identify objective thresholds and, hence, to delineate different categories of delamination severity in the entire bridge decks. The proposed integration methodology of NDT technologies and visual inspection results provided more reliable BDCI. The information that was sought to identify the parameters affecting the integration process was gathered from bridge engineers with extensive experience and intuition. The analysis process utilized the fuzzy set theory to account for uncertainties and imprecision in the measurements of bridge deck defects detected by IRT and GPR testing along with bridge inspector observations. The developed system and models should stimulate wider acceptance of IRT as a rapid, systematic and cost-effective evaluation technique for detecting bridge deck delaminations. The proposed combination of IRT and GPR results should expand their correlative use in bridge deck inspection. Integrating the proposed BDCI procedure with existing bridge management systems can provide a detailed and timely picture of bridge health, thus helping transportation agencies in identifying critical deficiencies at various service life stages. Consequently, this can yield sizeable reductions in bridge inspection costs, effective allocation of limited maintenance and repair funds, and promote the safety, mobility, longevity, and reliability of our highway transportation assets

State-Of-Research on Performance Indicators for Bridge Quality Control and Management

The present study provides a review of the most diffused technical and non-technical performance indicators adopted worldwide by infrastructure owners. This work, developed within the European COST Action TU 1406—“Quality specifications for roadway bridges, standardization at a European level,” aims to summarize the state-of-art maintenance scheduling practices adopted by bridge owners, mainly focusing on the identification and classification of the most diffused performance indicators (PIs). PIs are subdivided in technical and non-technical ones: for this latter subclass, PIs are classified in environmental, social and economic-targeted. The study aims to be a reference for researchers dealing with performance-based assessments and bridge maintenance and management practices

Fuzzy-based Condition Assessment Model for Offshore Gas Pipelines in Qatar

Condition assessment of offshore gas pipelines is a key player in pipeline operations and maintenance. They are used to ensure better decisions for repair and/or replacement and reduce failure possibilities. Information obtained from pipelines assessments are regularly used for scheduling upcoming maintenance and inspection activities. Therefore, it is valuable to have effective condition assessment of pipelines because failure incidents could lead to catastrophic economical and environmental consequences. Furthermore, current practices of assessing gas pipelines condition are considered too primitive and simplified. They mainly depend on experts' opinions in interpreting inspection data where the process is influenced by the human subjectivity and reasoning uncertainty. In another way, they need the detailed knowledge on translation of raw inspection data into valuable information. This will surely lead to decisions lacking thorough and extensive review of the most influential aspects on pipelines condition. To redress the weaknesses of the current practices and promote the performance of assessing offshore gas pipelines condition, this research proposes a new fuzzy-based methodology that utilizes hierarchical evidential reasoning (HER) for meticulous condition evaluation under subjectivity and uncertainty. The principle behind the posed structure is to establish an enhanced mechanism for the aggregation of different evidence bodies at multiple hierarchical levels in order to attain a reliable and exhaustive pipeline condition assessment. The essential characteristics of the proposed methodology are recapped in the following points. Firstly, the new approach suggests a more comprehensive hierarchy of the most influential factors affecting pipeline condition under three categories: physical, external, and operational. Secondly, this methodology is designed to consider the relative importance weights of all assessment factors in the hierarchy and to account for interdependencies among compared attributes. Thirdly, a hierarchical belief structure that utilizes evidential reasoning and fuzzy set theory is applied to grasp the uncertainty in pipeline evaluation. A model that utilizes HER can help combine different bodies of evidence at different hierarchical levels using Dempster-Shafer (D-S) rule of combination to obtain a detailed pipeline assessment. Fourthly, a condition assessment scale associated with rehabilitation actions is introduced as a framework for professionals to plan for future inspection and rehabilitation works. Finally, an automated, user-friendly, tool is developed for the propounded model to assess pipeline condition. Multiple sources of data were reached to provide a reliable assessment of pipe condition through the use of a structured questionnaire distributed among professionals in oil and gas industry in the studied region. This proposed model is compared and validated with historical inspection reports that were obtained from a local pipeline operator in Qatar. It is found that this model delivers satisfactory outcomes and forecasts offshore gas pipeline condition with an Average Validity Percent (AVP) of 97.6%. The developed fuzzy-based methodology is believed to offer a reliable condition assessment that optimizes data interpretation and usage of structured algorithms. Additionally, the introduced model and tool are compatible to researchers and practitioners such as pipeline engineers and consultants in order to prioritize inspection and rehabilitation for existing offshore gas pipelines. This immensely pictures the essence of infrastructure management to ameliorate cost and time optimization

A Defect-Based Approach for Detailed Condition Assessment of Concrete Bridges

Bridge condition assessment is one of the most essential elements of Bridge Management Systems (BMS). This is owing to the fact that available inputs from assessment reports are constantly interpreted for maintenance decisions and budget allocation to critical bridges within a region’s inventory. Thus, performing effective bridge assessment is vital to ensure safety and sustainability of the bridge infrastructure. In practice, the evaluation of concrete bridges is mostly conducted on the basis of visual inspection, which is associated with considerable subjectivity and uncertainty inherent in human judgments. Additionally, current bridge assessment practices were found to be oversimplified, with conclusions being often drawn in absence of in-depth review and consideration of critical factors. To remediate the existing shortcomings and ameliorate the bridge assessment process, this study proposes a fuzzy Hierarchical Evidential Reasoning (HER) approach for detailed condition assessment of concrete bridges under uncertainty. The essence of the suggested framework addresses the treatment and aggregation of uncertain measurements of detected bridge defects, in a systematic manner, to establish an enhanced platform for reliable and detailed bridge assessment. The significant features of this methodology can be summarized in the following points. First, the proposed approach utilizes a generic hierarchy that models the several levels of a concrete bridge under assessment; namely: bridge components, elements, and measured defects. Second, the proposed model is set to account for relative importance weights of all assessment factors in the hierarchical breakdown. Third, a novel HER assessment belief structure is employed to grip probabilistic uncertainty (ignorance) in bridge evaluation, whereas fuzzy uncertainty (subjectivity) is processed through a set of collectively exhaustive fuzzy linguistic variables. Forth, Dempster-Shafer (D-S) theory is eventually applied under the proposed HER framework for the purpose of accumulating supporting pieces of evidence in a comprehensive manner. The suggested model is implemented to arrive at detailed and informative bridge element condition ratings through data acquired from two case study bridges in Canada. As it benefits from a data oriented and structured algorithm, the developed defect-based model is believed to introduce a great deal of objectivity in an otherwise subjective area of infrastructure assessment. This falls within the ultimate goal of enhancing overall public safety and well-being

Improving water asset management when data are sparse

Ensuring the high of assets in water utilities is critically important and requires continuous improvement. This is due to the need to minimise risk of harm to human health and the environment from contaminated drinking water. Continuous improvement and innovation in water asset management are therefore, necessary and are driven by (i) increased regulatory requirements on serviceability; (ii) high maintenance costs, (iii) higher customer expectations, and (iv) enhanced environmental and health/safety requirements. High quality data on asset failures, maintenance, and operations are key requirements for developing reliability models. However, a literature search revealed that, in practice, there is sometimes limited data in water utilities - particularly for over-ground assets. Perhaps surprisingly, there is often a mismatch between the ambitions of sophisticated reliability tools and the availability of asset data water utilities are able to draw upon to implement them in practice. This research provides models to support decision-making in water utility asset management when there is limited data. Three approaches for assessing asset condition, maintenance effectiveness and selecting maintenance regimes for specific asset groups were developed. Expert elicitation was used to test and apply the developed decision-support tools. A major regional water utility in England was used as a case study to investigate and test the developed approaches. The new approach achieved improved precision in asset condition assessment (Figure 3–3a) - supporting the requirements of the UK Capital Maintenance Planning Common Framework. Critically, the thesis demonstrated that, on occasion, assets were sometimes misallocated by more than 50% between condition grades when using current approaches. Expert opinions were also sought for assessing maintenance effectiveness, and a new approach was tested with over-ground assets. The new approach’s value was demonstrated by the capability to account for finer measurements (as low as 10%) of maintenance effectiveness (Table 4-4). An asset maintenance regime selection approach was developed to support decision-making when data are sparse. The value of the approach is its versatility in selecting different regimes for different asset groups, and specifically accounting for the assets unique performance variables