In order to better assess the vulnerability of these structures, this study showcases the outcome of an observational analysis that utilizes biomimetical (bio-inspired) machine learning algorithms to predict the vulnerability and expected degree of damage in bridges in the aftermath of an extreme loading event (such as fire, flood, earthquake, etc.). With limited resources to properly maintain and upgrade transportation infrastructure, bridges often end up exceeding their expected service lifespan thus, becoming vulnerable to the adverse effects of aging and extreme loading conditions. It is demonstrated by this example that the developed approach in this paper can be effectively applied in practical risk assessment of bridges under different hazards considering the opinions of experts. According to results of the program, the expected bridge damage was moderate. For the case study, a single-tower cable-stayed bridge located in Tianjin, China was chosen. The output is the bridge damage level which might be minor, moderate and major. Each input variable has been estimated through the experts' questionnaire. The identified risks are taken as input variables to the fuzzy logic controller. In this study, sixteen identified risks which have influences on bridge damage are used. Many factors are important for risk assessment, but it is hard to quantify them because of the different opinions by experts. Risk sources which influence bridge functionality are identified by collecting and analyzing data of collapsed bridges. The controller is implemented under the NetBeans IDE 7.0.1 environment. For estimating damage levels of bridges, a fuzzy logic controller is developed in this paper. Fuzzy logic is suitable for dealing with uncertainties because of its robustness. In this paper, a risk assessment model for bridges is proposed based on fuzzy logic theory, so that it can be adapted by practicing engineers in bridge risk assessment. During the operation period, bridges are exposed to various risks which will cause damage and/or collapse of structures. Based on this study, some suggestions are provided such as perfect safety assurance system, standardized bridge maintenance and management, more intensive study on new technology and the natural disasters.īridges are very important elements of infrastructure systems. According to the bridge attribute information, arch bridge collapses more frequently than other types the longer the bridge is, the more frequently the collapse may occur from this statistic collapses in countryside are two times more than the ones in city. Unreasonable construction scheme is the main causes in artificial factors and the collapses of overload and collision has a large proportion in total. Especially the flood is a main cause besides earthquake. The analysis indicated that the bridge collapse is mostly caused by natural disaster. The information, including the cause, bridge type, bridge scale and location, were studied in statistical way. A total of 157 bridge collapses, not including the ones caused by earthquake, were collected from the public media report in China from January 2000 to March 2012.
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