Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost

In recent years, several probabilistic methods for assessing the performance of structural systems have been proposed. These methods take into account uncertainties associated with material properties, structural deterioration, and increasing loads over time, among others. When aging phenomena have significant effects on the life-cycle performance of the structure, it becomes essential to perform actions to maintain or improve structural safety, in agreement with the system requirements and available funds. Various optimization methods and performance indicators have been proposed for the determination of optimal maintenance plans for simple and complex systems. The aim of this paper is twofold: (a) to assess and compare advantages and drawbacks of four different performance indicators related to multi objective optimization of maintenance schedules of deteriorating structures, and (b) to assess the cost-efficiency of the associated optimal solutions. Two annual performance indicators, annual reliability index and annual risk, and two lifetime performance indicators (i.e. availability and hazard functions) are used in conjunction with total maintenance cost for evaluating Pareto fronts associated with optimal maintenance schedules of deteriorating structures. Essential maintenance actions are considered and optimization is performed by using genetic algorithms. The approach is illustrated on an existing deteriorating bridge superstructure

Bridge Management System with Integrated Life Cycle Cost Optimization

In recent years, infrastructure renewal has been a focus of attention in North America and around the world. Municipal and federal authorities are increasingly recognizing the need for life cycle cost analysis of infrastructure projects in order to facilitate proper prioritization and budgeting of maintenance operations. Several reports have highlighted the need to increase budgets with the goal of overcoming the backlog in maintaining infrastructure facilities. This situation is apparent in the case of bridge networks, which are considered vital links in the road network infrastructure. Because of harsh environments and increasing traffic volumes, bridges are deteriorating rapidly, rendering the task of managing this important asset a complex endeavour. While several bridge management systems (BMS) have been developed at the commercial and research level, they still have serious drawbacks, particularly in integrating bridge-level and network-level decisions, and handling extremely large optimization problems. To overcome these problems, this study presents an innovative bridge management framework that considers network-level and bridge-level decisions. The initial formulation of the proposed framework was limited to bridge deck management. The model has unique aspects: a deterioration model that uses optimized Markov chain matrices, a life cycle cost analysis that considers different repair strategies along the planning horizon, and a system that considers constraints, such as budget limits and desirable improvement in network condition. To optimize repair decisions for large networks that mathematical programming optimization are incapable of handling, four state-of-the art evolutionary algorithms are used: Genetic algorithms, shuffled frog leaping, particle swarm, and ant colony. These algorithms have been used to experiment on different problem sizes and formulations in order to determine the best optimization setup for further developments. Based on the experiments using the framework for the bridge deck, an expanded framework is presented that considers multiple bridge elements (ME-BMS) in a much larger formulation that can include thousands of bridges. Experiments were carried out in order to examine the framework’s performance on different numbers of bridges so that system parameters could be set to minimize the degradation in the system performance with the increase in numbers of bridges. The practicality of the ME-BMS was enhanced by the incorporation of two additional models: a user cost model that estimates the benefits gained in terms of the user cost after the repair decisions are implemented, and a work zone user cost model that minimizes user cost in work zones by deciding the optimal work zone strategy (nighttime shifts, weekend shifts, and continuous closure), also, decides on the best traffic control plan that suits the bridge configuration. To verify the ability of the developed ME-BMS to optimize repair decisions on both the network and project levels, a case study obtained from a transportation municipality was employed. Comparisons between the decisions provided by the ME-BMS and the municipality policy for making decisions indicated that the ME-BMS has great potential for optimizing repair decisions for bridge networks and for structuring the planning of the maintenance of transportation systems, thus leading to cost savings and more efficient sustainability of the transportation infrastructure

Steel-Concrete Composite Bridges: Design, Life Cycle Assessment, Maintenance, and Decision-Making

[EN] Steel-concrete composite bridges are used as an alternative to concrete bridges because of their ability to adapt their geometry to design constraints and the possibility of reusing some of the materials in the structure. In this review, we report the research carried out on the design, behavior, optimization, construction processes, maintenance, impact assessment, and decision-making techniques of composite bridges in order to arrive at a complete design approach. In addition to a qualitative analysis, a multivariate analysis is used to identify knowledge gaps related to bridge design and to detect trends in research. An additional objective is to make visible the gaps in the sustainable design of composite steel-concrete bridges, which allows us to focus on future research studies. *eresults of this work show how researchers have concentrated their studies on the preliminary design of bridges with a mainly economic approach, while at a global level, concern is directed towards the search for sustainable solutions. It is found that life cycle impact assessment and decision-making strategies allow bridge managers to improve decision-making, particularly at the end of the life cycle of composite bridges.This study was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (DIMALIFE Project BIA2017-85098-R).Martínez-Muñoz, D.; Martí Albiñana, JV.; Yepes, V. (2020). Steel-Concrete Composite Bridges: Design, Life Cycle Assessment, Maintenance, and Decision-Making. Advances in Civil Engineering. 2020:1-13. https://doi.org/10.1155/2020/8823370S113202

Prediction and mitigation of scour and scour damage to Vermont bridges

Over 300 Vermont bridges were damaged in the 2011 Tropical Storm Irene and many experienced significant scour. Successfully mitigating bridge scour in future flooding events depends on our ability to reliably estimate scour potential, design safe and economical foundation elements accounting for scour potential, design effective scour prevention and countermeasures, and design reliable and economically feasible monitoring systems, which served as the motivation for this study. This project sought to leverage data on existing Vermont bridges and case studies of bridge scour damage, and integrate available information from stream geomorphology to aid in prediction of bridge scour vulnerability. Tropical Storm Irene’s impact on Vermont bridges was used as a case study, providing damage information on a wide range of bridges throughout the State. Multiple data sources were combined in an effort to include data, which represents the complex, interconnected processes of stream stability and bridge scour, then identify and incorporate feature that would be useful in a probabilistic model to predict bridge susceptibility to scour damage. The research also sought to identify features that could be included in inspections and into a scour rating system that are capable of assessing network-level scour vulnerability of bridges more holistically. This research also sought to review existing scour countermeasures and scour monitoring technologies available in the literature and examine efficacy of new, indirect scour countermeasures and passive scour monitoring techniques. The specific objectives of this research were to: (1) review the literature and identify methods/technologies that are adaptable to Vermont; (2) analyze Tropical Storm Irene bridge damage information and observations by collecting and geo-referencing all available bridge records and stream geomorphic assessment data into a comprehensive database for identifying features that best represent bridge scour damage; (3) conduct watershed analysis on all bridges, including creation of stream power data to assess if watershed stream power improves the prediction of bridge scour damage; and (4) investigate new scour countermeasures and monitoring technologies, and provide recommendations on implementations

Developing Condition-Based Triggers for Bridge Deck Maintenance and Rehabilitation Treatments

The bridges in the U.S. highway system suffer from deficiencies in both their structural condition and functionality. In an effort to improve the condition of bridges, highway agencies continually seek effective and efficient approaches to maintenance and rehabilitation (M&R) treatments for their bridges. However, one drawback to new approaches is that highway agencies have long relied on the subjective judgment of their engineers to determine the time or condition at which to implement the treatments as well as the types of treatments to be applied. The literature shows that previous researchers mainly focused on time-based M&R strategies, but there have been some efforts toward developing condition-based strategies, such as the Indiana Bridge Management System (IBMS). While IBMS and similar systems were laudable efforts, they also were developed on the basis of the judgment and experience of bridge management personnel and were not data-driven

Comparison between Optimization and Heuristic Methods for Large-Scale Infrastructure Rehabilitation Programs

Civil infrastructure systems are the foundation of economic growth and prosperity in all nations. In recent years, infrastructure rehabilitation has been a focus of attention in North America and around the world. A large percentage of existing infrastructure assets is deteriorating due to harsh environmental conditions, insufficient capacity, and age. Ideally, an assets management system would include functions such as condition assessment, deterioration modeling, repair modeling, life-cycle cost analysis, and asset prioritization for repair along a planning horizon. While many asset management systems have been introduced in the literature, few or no studies have reported on the performance of either optimization or heuristic tools on large-scale networks of assets. This research presents an extensive comparison between heuristic and genetic-algorithm optimization methods for handling large-scale rehabilitation programs. Heuristic and optimization fund-allocation approaches have been developed for three case studies obtained from the literature related to buildings, pavements, and bridges with different life cycle cost analysis (LCCA) formulations. Large-scale networks were constructed for comparing the efficiency of heuristic and optimization approaches on large-scale rehabilitation programs. Based on extensive experiments with various case studies on different network sizes, the heuristic technique proved its practicality for handling various network sizes while maintaining the same efficiency and performance levels. The performance of the genetic algorithm optimization approach decreased with network size and model complexity. The optimization technique can provide a high performance level, given enough processing time

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and use of a performance-based approach in transportation planning and programming. This research introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to (1) improve bridge management, (2) support state departments of transportation with their transition efforts to comply with the MAP-21 requirements, (3) determine short- and long-term intervention strategies and funding requirements, and (4) facilitate trade-offs between funding levels and performance. The proposed methodology focuses on one transportation asset class (i.e., bridge) and is structured around the following five modules: 1. Data Processing Module, 2. Improvement Module, 3. Element-level Optimization Module, 4. Bridge-level Optimization Module, and 5. Network-level Optimization Module. To overcome computer memory and processing time limitations, the methodology relies on the following three distinct screening processes: 1. Element Deficiency Process, 2. Alternative Feasibility Process, and 3. Solution Superiority Screening Process. The methodology deploys an independent deterioration model (i.e., Weibull/Markov model), to predict performance, and a life-cycle cost model, to estimate life-cycle costs and benefits. Life-cycle (LC) alternatives (series of element improvement actions) are generated based on a new simulation arrangement for three distinct improvement types: 1. maintenance, repair and rehabilitation (preservation); 2. functional improvement; and 3. replacement. A LC activity profile is constructed separately for each LC alternative action path. The methodology consists of three levels of optimization assessment based on the Pareto optimality concept: (1) an element-level optimization, to identify optimal or near-optimal element intervention actions for each deficient element (poor condition state) of a candidate bridge; (2) a bridge-level optimization, to identify combinations of optimal or near-optimal element intervention actions for a candidate bridge; and (3) a network-level optimization, following either a top-down or bottom-up approach, to identify sets of optimal or near-optimal element intervention actions for a network of bridges. A robust metaheuristic genetic algorithm (i.e., Non-dominated Sorting Genetic Algorithm II, [NSGA-II]) is deployed to handle the large size of multi-objective optimization problems. A MATLAB-based tool prototype was developed to test concepts, demonstrate effectiveness, and communicate benefits. Several examples of unconstrained and constrained scenarios were established for implementing the methodology using the tool prototype. Results reveal the capability of the proposed EB-MOO methodology to generate a high quality of Pareto optimal or near-optimal solutions, predict performance, and determine appropriate intervention actions and funding requirements. The five modules collectively provide a systematic process for the development and evaluation of improvement programs and transportation plans. Trade-offs between Pareto optimal or near-optimal solutions facilitate identifying best investment strategies that address short- and long-term goals and objective priorities

MAT-714: CONDITION ASSESSMENT AND DETERIORATION PREDICTION TOOLS FOR CONCRETE BRIDGES: A NEW LOOK

Structural problems created by corrosion, ageing, aggressive environments, material defects and unforeseen mechanical or seismic loads can compromise the serviceability and safety of bridges. The importance of an effective bridge-management system (BMS) cannot be overstated, especially in light of the recent collapse of bridges in North America and elsewhere. Several technologies are available for assessing the condition of concrete bridges and a number of deterioration models are used to predict future bridge conditions and estimate associated funding requirements. This paper critically reviews the different available condition assessment and deterioration prediction approaches for concrete bridges. The potential applications of condition assessment technologies with particular focus on their advantages and limitations are presented. The various types of deterioration models are discussed and compared. The findings indicate that: (i) non-destructive testing (NDT) methods and structural health monitoring (SHM) systems can play a major role in effectively evaluating the conditions of concrete bridges; (ii) mechanistic models for deterioration prediction embrace a reliability-based approach that can provide bridge owners and maintenance personnel with an improved tool to assess bridge conditions and to make decisions regarding their maintenance; and (iii) automated data collection and interpretation analysis is needed for improved BMS. The challenges associated with the different technologies and models are outlined. Furthermore, to empower bridge asset managers in making more informed decisions, recommendations are made on the selection of appropriate evaluation and prediction models that meet desired service goals