1,721,177 research outputs found
Bridge Condition Assessment Using Supervised Decision Trees
Full text linkBridges are at risk from aging, fatigue and deterioration processes. Many countries are facing with large stocks of existing bridges approaching the end of the service life and the preservation of their structural performance and functional adequacy is a priority for administrations, public authorities, and decision makers dealing with bridge condition rating and infrastructure management. Visual inspections are at the base of an effective and reliable bridge condition assessment. Inspection strategies and procedures determine how results are returned, stored and managed leading to the formulation of different bridge condition indicators. The collection of information over time provides a great amount of bridge data which can be properly elaborated to get useful insights for supporting the decision-making process. Classification tools, such as Decision Trees (DTs) can be exploited to prioritize maintenance and rehabilitation interventions within the transportation network. This paper presents the application of a supervised DT for the assessment of bridge condition. The proposed approach is applied to classification in Good, Fair, or Poor status of a stock of existing bridges located in California. The DT is trained using visual inspection results stored in public United States National Bridge Inventory (NBI)
Role of the earthquake scenario on life-cycle seismic resilience of aging bridge networks
Full text linkThe time-variant structural capacity of critical infrastructure facilities is an important performance indicator in the definition of reliable policies for emergency management and long-term planning of risk mitigation strategies. This paper investigates the seismic resilience of transportation road networks based on a probabilistic framework for life-cycle seismic assessment of deteriorating bridges under prescribed earthquake scenarios with different magnitude and epicenter location. The seismic damage suffered by the exposed bridges and the effects of repair actions are related to traffic limitations and vehicle restrictions implemented over the network. The life-cycle seismic resilience is evaluated under post-event structural recovery of each bridge processes and functionality restoration of the network traffic capacity. The proposed framework is applied to reinforced concrete bridges exposed to seismic hazard and chloride-induced corrosion in a highway network with detour and re-entry link. The results emphasize the role of network topology, earthquake scenario, and time-variant structural deterioration of spatially distributed bridges on the life-cycle functionality and seismic resilience of aging road networks
Nonlinear Structural Analysis of PC Bridge Deck Beams
No full textThe residual structural performance of existing concrete bridges is investigated within the BRIDGE|50 research project. The activities include full-scale load tests on several prestressed concrete (PC) bridge deck beams sampled from a 50-year-old concrete viaduct recently dismantled in Italy. This paper presents the formulation of a PC beam finite element and the preliminary results of numerical simulations performed to reproduce the structural behavior of the first two tested beams. The purpose of the numerical investigation is to validate the nonlinear analysis model and support a proper planning of the future load tests
Probabilistic life-cycle seismic resilience assessment of aging bridge networks considering infrastructure upgrading
No full textThis paper presents a probabilistic framework for life-cycle seismic resilience assessment of aging bridges and transportation road networks subjected to infrastructure upgrade. The proposed framework accounts for the uncertainties in damage occurrence of vulnerable deteriorating bridges and restoration rapidity of the overall system functionality. The time-variant bridge fragilities and the damage combinations probability are evaluated considering different earthquake magnitudes and epicenter locations that define the seismic scenario. Traffic analyses are carried out to assess in probabilistic terms the network functionality profiles, the corresponding resilience levels, and a damage-based measure of life-cycle resilience. The effects of structural deterioration, seismic damage, and post-event repair actions under uncertainty are related to traffic restrictions applied over the network. The framework is applied to reinforced concrete bridges exposed to chloride-induced corrosion and simple road networks with a single bridge or two bridges in series under different earthquake scenarios. The effect of network upgrading is also investigated by adding road segments with a vulnerable bridge to strengthen the network connectivity and improve the lifetime system resilience. The results show the capability of the proposed resilience framework in quantifying the detrimental effects of structural deterioration at the network scale and the beneficial consequences of infrastructure investments, such as enhancing the network redundancy with the construction of an additional highway branch
Life-Cycle Assessment of Deteriorating RC Bridges Using Artificial Neural Networks
No full textLife-cycle structural assessment of existing bridges under aging and deterioration processes is of paramount importance for authorities managing road networks. In fact, in most developed countries many bridges and infrastructure facilities are approaching 50 years of lifetime or are even older, and a significant percentage of them are prone to be rated as structurally deficient. In this context, a quick and reliable estimation of the bridge condition is needed for the prioritization of maintenance and repair interventions and the optimal allocation of resources over large bridge stocks. In the last decades, computerized bridge management systems (BMSs) emerged worldwide to support decision makers in the definition of operational management policies. BMSs often incorporate soft computing tools, such as artificial neural networks (ANNs), which can provide reliable bridge assessments over time under uncertainty on the basis of limited data stored in bridge databases. This paper proposes an approach to the life-cycle assessment of deteriorating reinforced concrete (RC) bridges based on ANNs. Two-layer ANNs are formulated and trained for this purpose. The proposed approach is preliminarily applied to a time-variant structural capacity assessment of a RC bridge deck cross-section under chloride-induced corrosion to compare the results obtained with both full and limited input datasets. Finally, the methodology is applied to the prediction over time of the condition state of a group of existing RC bridges based on the information stored in the US National Bridge Inventory
Seismic resilience of deteriorating RC bridges and road networks under climate change
No full textThis paper investigates the life-cycle seismic resilience of aging road networks with reinforced concrete (RC) bridges under the effects of climate change. The physical damage suffered by the exposed bridges is related to traffic limitations implemented over the network. A probabilistic framework is proposed to aggregate the time-variant seismic capacity assessment of RC structures exposed to chloride-induced corrosion with the traffic response of the transportation network. The life-cycle seismic resilience of a simple road network is evaluated based on the restoration of the network functionality guaranteed by the post-event recovery of the damaged bridge. The results highlight the detrimental effects of the progressive increase in the deterioration rate induced by climate change, impairing the seismic capacity of single bridges and, in turn, the seismic resilience of the overall transportation system
Life-Cycle Design, Assessment and Maintenance of Structures and Infrastructure Systems
No full textStructural engineering is facing a profound change toward a life-cycle oriented design philosophy in order to address the continuously increasing societal, political, economic, and environmental demand for sustainable structural and infrastructural facilities that minimize risks arising from aging, deterioration, and natural and human-made hazards. Life-Cycle Design, Assessment, and Maintenance of Structures and Infrastructure Systems is a state-of-the-art comprehensive report outlining the current status and research needs in life-cycle of civil structure and infrastructure systems. This book examines - Physical, chemical, and mechanical processes involved in the degradation mechanisms of concrete and steel structures located in severe environments; - Methods and strategies for life-cycle design and assessment of deteriorating structural systems under uncertainty; - Life-cycle management concepts for structures and infrastructure networks under uncertainty and the application of such concepts in management process; and - Principles and implications associated with the scheduling and application of maintenance policies for deteriorating structures and infrastructure networks. This book serves as a valuable resource to engineers, managers, and government agencies concerned with life-cycle design and maintenance of civil structures and infrastructure systems
Design, Assessment, Monitoring and Maintenance of Bridges and Infrastructure Networks
No full textThis book collects the extended versions of selected papers presented at IABMAS 2012 and invited papers originally published in a Special Issue of Structure and Infrastructure Engineering. These papers provide significant contributions to the process of making more rational decisions in bridge design, assessment, monitoring and maintenance. The editors would like to thank the authors for their contributions and hope that this collection of papers will represent a valuable reference for scientific research and engineering applications in the fields of design, assessment, monitoring, and maintenance of bridges and infrastructure networks
Life-cycle seismic performance prediction of deteriorating RC structures using artificial neural networks
No full textThe paper deals with the life-cycle performance prediction of deteriorating Reinforced Concrete (RC) structures by means of Artificial Neural Networks (ANNs). A three-layer ANN is developed and trained to capture the overall system performance based on limited amount of information related to local damage of some components, typically obtained from the results of visual inspections. The training datasets are formed to incorporate the results from several inspections carried out over given observation time intervals and to accommodate predictions over the remaining structural lifetime. The proposed ANN is applied to the life-cycle seismic capacity assessment of a three-story RC frame under chloride-induced corrosion
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