With the recent developments of several bridge managements systems and their wide-spread use, bridge engineers are realizing the importance of systematic and well planned investments and appropriate management. However the results are far from satisfactory. Bridge management systems need more effective policy analysis tools that can take advantage of the vast amounts of available information to be more efficient. The objective of this research is to develop a policy analysis tool, which is generic in nature and can be applied to any bridge management system provided all the appropriate data is available. In particular, this policy analysis tool is geared to suit policy making, planning and budgeting for the interstate bridges in the state of Virginia. The policy analysis tool developed in this research is a discrete event simulation model capable of extracting information from text files in the Pontis Data Interchange format and simulate user defined element level policies. The model testing was performed using the interstate bridges of the Salem district in Virginia. All the relevant information was extracted from their PONTIS databases. Several scenarios with varying network policies were simulated. The results indicate the validity and the accuracy of the model. The policy analysis tool is a useful addition to the existing policy analysis tools and is capable of handling probabilistic distributions of data instead of single value averages. This will enable the tool to capture more information thereby making the simulation model more realistic. The general framework that was developed here can be applied to any infrastructure problem, and eventually it should be possible to achieve a discrete event simulation based integrated infrastructure management system. / Master of Science

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.

Bridges are vital links in many transport networks and represent a big capital investment for both governments and taxpayers. They have to be managed in a way that ensures society's needs are optimally met. In many countries, bridges are mainly managed using bridge management systems (BMSs). Although many BMSs contain some forms of life-cycle costing (LCC), the use of LCC in bridge engineering is scarce. LCC in many BMSs has mainly been applied within the bridge operation phase, even though it has several useful applications within the bridge entire life, from cradle to grave. This licentiate thesis discusses the need of a BMS with integrated comprehensive LCC tools that can assist decision-makers at all levels and within all phases in selecting the most cost-effective alternative from an array of applicable alternatives. The thesis introduces the Swedish Bridge and Tunnel Management System (BaTMan). Acomprehensive integrated LCC implementation scheme is illustrated, taking into account the bridge investment and management process in Sweden. The basic LCC analytical tools as well as other helpful LCC techniques are addressed. Detailed case studies for real bridges at different investment phases are presented to demonstrate the recent improvement of BaTMan practically in the LCC integration. Cost records for 2,508 bridges extracted from BaTMan inventory data are used as input data in the presented case studies. Considering the same records, the average real and anticipated initial costs of different bridge types in Sweden will schematically be presented. The thesis introduces a bridge LCC program developed over this research named "BaTMan-LCC". The reason for which this program was developed is to combine all possible LCC applications for bridges in one tool and facilitate its implementation. The sensitivity analysis as well as the LCC saving potential highlighted in the presented case studies emphasizes the feasibility and the possibility of developing BaTMan to accommodate the applications of BaTMan-LCC. / QC 20120301 / ETSI

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.

Bridges, critical components of a nation’s infrastructure network, are vulnerable to deterioration from aging, fatigue, and external events like earthquakes or impacts. These challenges, coupled with limited maintenance resources, create the need for efficient and expeditious predictive maintenance strategies. The present thesis focuses on the development and application of Decision Support Systems (DSS) for the optimization of bridge stocks management and upkeep. The developed DSS introduces a streamlined bridge risk assessment methodology that leverages structural reliability and advanced probabilistic models to perform risk projections, accounting for the degradation in both the short and long-term. The methodology is based on the general definition of risk as the combination of Hazard, Vulnerability – jointly computed by means of structural reliability – and Exposure. Within the structural reliability evaluation, two key components are integrated into the proposed procedure: a Markov Chain-based degradation model that forecasts the progression of existing damage, and a Bayesian model that predicts the appearance of new structural defects over time. Hence, the DSS provides a projection of structural reliability over different timeframes. Within the context of future bridge maintenance interventions, such projected reliability, combined with the potential consequences of the collapse (Exposure) and intervention costs, computes a Priority Index for each possible intervention scenario. The result is a cost-efficient ranking system that enables data-driven decisions, optimizing both maintenance priorities and resource allocation. The efficiency of the DSS is demonstrated through practical examples on real life bridges, simulating the daily tasks of an infrastructure manager. In addition, contextually to the inclusion of railway bridges in the DSS, the thesis addresses the need for streamlined procedures for their swift risk assessment. A quantitative method is proposed to calculate in an expeditious and low-input manner the structural risk of railway bridges by integrating once again structural reliability and exposure. Specifically, for the evaluation of the former for aging masonry arch railway bridges, which constitute a significant portion of the Italian railway infrastructure stock, a simplified approach is introduced to estimate their load-carrying capacity based on minimal structural data: span, rise-to-span ratio and design code. This method applies the Static Theorem to determine the most conservative geometry compatible with the original design code, estimating the load rating factor with respect to modern freight loads. A parametric analysis is conducted for various spans, rise-to-span ratios, and design codes, with results presented in easy-to-use charts and practical guidelines to help railway operators rank their bridges based on capacity deficits. This research advances the field of bridge management by offering sophisticated yet practical tools that enhance decision-making, ensuring safer and more efficient management of aging infrastructure.