A decision support methodology for rehabilitation management of concrete bridges

Managing the existing bridge infrastructure has become a major social and economic concern in North America. This is due to the critical conditions of the deteriorated bridges and the limited funds available to repair their deficiencies. Most transportation agencies make bridge investment decisions based on a combination of some form of quantitative data analysis and the subjective judgments of decision and policy makers. The subjective nature of the decision making process easily raises questions about whether the investment decisions are being developed in a fair, equitable and systematic manner. This dissertation presents a decision support methodology developed for the rehabilitation management of concrete bridges in general, and for bridge decks in particular. A probabilistic bridge condition assessment method is developed. This method is consistent with the current practice in bridge inspection and the Markovian approach to model deterioration. A means to rank bridge projects is presented, which makes use of a hierarchy structure to represent the problem and rank the different bridge projects using the Multi Attribute Utility Theory (MAUT). A method to evaluate the available rehabilitation strategies is discussed. This method uses a modified Analytic Hierarchy Process (AHP) and the Monte Carlo simulation technique to evaluate the weights for the different rehabilitation strategies available for each project. A decision making technique to select a recommended work program that maximizes benefits to the network and to the users is developed. The developed methodology has the potential to be extended to other bridge components and to be the foundation for a comprehensive bridge management system. The significant features of this methodology can be summarized as follows: (1) It is consistent with the current practice in bridge management condition assessment and deterioration modeling. (2) It employs a multiple-criteria decision making process; (3) it has the flexibility to allow engineers to utilize their experience and judgment in the decision making process; and (4) It combines the network and the project levels of the bridge management process and performs effectively within a limited budget

Relaxation of prestressed steel used in construction of the confederation bridge

Relaxation is one of the main sources of time-dependent stress losses in prestressed concrete construction. In the present study, an experimental investigation of the stress relaxation of the prestressing steel used in construction of the Confederation Bridge is undertaken. Relaxation is measured by monitoring the reduction in stress level in an elastically strained specimen and maintained at a constant extension. Two testing methods are adopted for the experimental program, namely, the release method and the lateral deflection method. A special setup is built for each of the two testing methods. Six specimens are tested using the release testing method. Of these, two specimens are tested at each of the three initial stress levels: 60%, 70% and 80% of the ultimate strength of the steel. The lateral deflection testing method is conducted on two specimens at two initial stress levels of 60% and 70% of the ultimate strength of the steel. The experimental results of this research are compared with steel stress relaxation estimated by the PCI expression and the CEB-FIP Code equation. These comparisons show that the available expressions underestimate the magnitude of stress relaxation losses. Based on the results of this research, an alternative equation to estimate stress relaxation of steel is proposed

A Markovian-Genetic Algorithm Model for Predicting Pavement Deterioratio

Pavement structures are constantly deteriorating due to many distresses, for instance cracks and rutting that are initiated and expanded. Deterioration models of pavement structures is an important component of pavement management systems (PMS). The deterioration of pavements has been extensively modeled using Markov chains. This paper aims at formulating a more efficient deterioration model to predict the condition of pavement sections. It is proposed to accomplish this by developing a Markovian deterioration model coupled with a meta-heuristic search optimization method, namely genetic algorithms (GA). An essential component of the Markov chain model is the transition probability matrix. In the proposed model, a standard percentage prediction method was used to calculate the transition probabilities. This is then calibrated by integrating the GA method with the Markov chain. The model is based on the historical international roughness index (IRI) data retrieved from the long-term pavement performance (LTPP) database. To test the validity of the method, a real-life case study is used and the performance of the developed model was assessed using both validation and testing data. For predicting pavement conditions, this study concluded that calibrating calculated transition probabilities using meta-heuristic optimization results in better performance than developing the transition probabilities using classical methods. The Markovian-GA model developed in the present study can be used to predict the future condition of pavement facilities in order to assist engineers in planning the optimum maintenance and rehabilitation (M&R) actions

Editorial : automation and artificial intelligence in construction and management of civil infrastructure

The construction industry, similar to other industries, has been moving toward automation and implementation of AI tools to enhance analysis, management and decision-making. The construction industry typically takes longer than other industries in inaugurating innovation and advanced technologies due to the complex nature of the industry. Research effort is deemed necessary to understand needs and overcome challenges in terms of studying the capabilities and potential of existing tools and technologies in areas related to construction engineering and management. The current Research Topic attempted to collect relevant research work in terms of automation and AI application in constructing new assets and managing existing infrastructure. Further research is needed to standardize these processes and align research work with current needs

UAV-Based Remote Sensing Applications for Bridge Condition Assessment

Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection techniques, such as visual inspection, are time-consuming, subjective, and often incomplete. Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. The primary objective of this study is to conduct a comprehensive review of the application of UAVs in bridge condition monitoring, used in conjunction with remote sensing technologies. Remote sensing technologies such as visual imagery, infrared thermography, LiDAR, and other sensors, integrated with UAVs for data acquisition are analyzed in depth. This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems. In addition to comparison of stand-alone and integrated NDT-UAV methods, the facilitation of bridge inspection using UAVs is thoroughly discussed in the present article in terms of ease of use, accuracy, cost-efficiency, employed data collection tools, and simulation platforms. Additionally, challenges and future perspectives of the reviewed UAV-NDT technologies are highlighted

Decision support method for multi-criteria selection of bridge rehabilitation strategy

Bridge management is the decision-making process for selecting and prioritizing the actions necessary to maintain a bridge within acceptable limits of safety and serviceability. The current decision-making approach for bridge management is based on optimizing the life cycle cost of the structure. This is a single criterion decision-making process which does not include the indirect impact of the maintenance, repair and replacement actions. Sound bridge management decisions should be made through balanced consideration of multiple and conflicting criteria. This requirement motivated the development of a multi-criteria decision support method for bridge deck management. The method is based on a modified analytic hierarchy process (AHP) to evaluate and rank alternative bridge rehabilitation strategies. The modified AHP provides an effective analytical tool to deal with complex decision making and has the following features: (1) multi-criteria decision-making process; (2) accounts for the uncertainty associated with the pairwise comparison values; and (3) provides a sensitive evaluation of consistency in judgements. The proposed decision support method is a rational decision-making technique for bridge management. The method practicality and validity is demonstrated using a real case study from the industry.AHP, bridge, decision, rehabilitation,

Common practices in assessing conditions of concrete bridges

Bridge condition assessment is an essential step in bridge management. To ensure safety and serviceability of bridge infrastructure, accurate condition assessment is needed to provide basis for bridge Maintenance, Repair, and Replacement (MRR) decisions. In Canada and the United States, visual inspection is the common practice to evaluate a bridge condition. Meanwhile, this practice is limited to detect surface defects and external flaws. For subsurface defects, Non-Destructive Testing and Evaluation (NDT&E) technologies are being used to supplement visual inspection. This paper reviews the common practices in assessing concrete bridges’ conditions and discusses the limitations of available condition assessment models. Further, this research studies six NDT&E techniques and establishes a set of selection criteria which is utilized to compare each technique in terms of providing the best inspection results. Based on the comparison, it is found that Ground Penetrating Radar (GPR) appears to be the most appropriate NDT&E techniques for inspection of concrete bridges. Thus, this paper recommends integrating GPR technology with the dominant visual inspection practice in order to establish a more accurate overall bridge condition rating system where surface and subsurface defects are assessed

Performance-based contracting for maintaining transportation assets with emphasis on bridges

With a large number of aging transportation infrastructure assets in North America and the growing problem of deterioration across the globe, managing these assets have been the subject of ongoing research. There is an overwhelming amount of maintenance and rehabilitation works to be done and selecting a suitable maintenance, repair or replacement (MRR) strategy is one of the most challenging tasks for decision makers. Limited budget and resources are even making the decision making process more challenging. Maintaining infrastructure to the highest possible condition while investing the minimal amount of money has promoted innovative contracting approaches. Transportation agencies have increased private sector involvement through long term performance-based maintenance contracts or what is called Performance-Based Contracting. PBC is a type of contract that pays a contractor based on the results achieved, not on the methods for performing the maintenance work. By looking into the literature, it is observed that agencies are expanding the amount of contracting they do in order to maintain and achieve a better standard of infrastructure facilities. Therefore, the objective of this paper is to study and review performance-based contracting for transportation infrastructure with emphasis on bridge assets

Common practices in assessing conditions of concrete bridges

Bridge condition assessment is an essential step in bridge management. To ensure safety and serviceability of bridge infrastructure, accurate condition assessment is needed to provide basis for bridge Maintenance, Repair, and Replacement (MRR) decisions. In Canada and the United States, visual inspection is the common practice to evaluate a bridge condition. Meanwhile, this practice is limited to detect surface defects and external flaws. For subsurface defects, Non-Destructive Testing and Evaluation (NDT&E) technologies are being used to supplement visual inspection. This paper reviews the common practices in assessing concrete bridges’ conditions and discusses the limitations of available condition assessment models. Further, this research studies six NDT&E techniques and establishes a set of selection criteria which is utilized to compare each technique in terms of providing the best inspection results. Based on the comparison, it is found that Ground Penetrating Radar (GPR) appears to be the most appropriate NDT&E techniques for inspection of concrete bridges. Thus, this paper recommends integrating GPR technology with the dominant visual inspection practice in order to establish a more accurate overall bridge condition rating system where surface and subsurface defects are assessed