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Summary: Deflection is an important issue in bridge structural health monitoring. An accurate deflection–vehicle load–temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflection–vehicle load–temperature correlation, the correlation modeling method needs further studies. A novel deflection–vehicle load–temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time‐continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established. Taking a suspension bridge in China as an example, the prediction accuracy of short‐, medium‐, and long‐term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature‐ and vehicle‐induced deflection components is proposed. The results show that the predicted deflection of the short‐term correlation model is basically consistent with the real‐time monitoring data, while the medium‐ and long‐term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature‐ and vehicle‐induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment. [ABSTRACT FROM AUTHOR]

Hangers or suspenders of a suspension bridge are the primary load-carrying members and are vital to the structural integrity and service life of the bridge. Site-specific vehicle loads monitored by the weigh-in-motion system can assist to obtain the operational cyclic stresses of hangers. Differing from most existing studies, herein, a framework for fatigue performance investigation for hangers of suspension bridges is proposed utilizing the full information of the weigh-in-motion data. This framework includes four steps: (1) generate influence surfaces for hangers, (2) reconstruct vehicular loading flows based on the weigh-in-motion data, (3) calculate time histories of hanger tension forces, and (4) evaluate fatigue damages and predict fatigue lives. Critical issues, such as the loading configuration of trucks, the threshold of the gross vehicle weight, and the time step for stress calculation, have been studied and discussed in detail. Based on 8-month weigh-in-motion data of a prototype suspension bridge, it is shown that the fatigue damage of hangers can be evaluated day by day, and subsequently the fatigue lives can be predicted. The correlation between the fatigue damages and vehicular loads is also investigated in this study. [ABSTRACT FROM AUTHOR]

A time-dependent fatigue reliability assessment approach is proposed for welded details of orthotropic steel decks (OSDs) using long-term strain monitoring data. The fatigue reliability limit function of the welded details is established based on the Eurocode specifications. Depending on the distribution characteristics of the measured daily equivalent stress range, either the lognormal distribution or Gaussian mixture model (GMM) is selected to quantify its uncertainty. Subsequently, the fatigue reliability can be calculated using either an explicit formula or the Monte Carlo method. This proposed approach is applied for the fatigue reliability evaluation of two rib-to-deck and two rib-to-rib welded fatigue details of an in-service suspension bridge. The results show that the reliability indices decrease significantly with bridge's service life. Except for a rib-to-deck detail, all other three welded details cannot meet the target fatigue reliability during this bridge's 100-year service life. The proposed approach can help bridge owners and operators make informed decisions regarding maintenance and repair of potential fatigue cracks. [ABSTRACT FROM AUTHOR]

This paper presents a sensitivity and reliability analysis of a self-anchored suspension bridge by applying a new hybrid method proposed by the authors based on integration of the Latin hypercube sampling technique (LHS), artificial neural network (ANN), first-order reliability method (FORM), Pearson's linear correlation coefficient (PLCC), and Monte Carlo simulation with important sampling technique (MCS-IS). The framework consists of three stages of analysis: (1) selection of training, validation, and test datasets for establishing an ANN model by the LHS technique; (2) formulation of a performance function from the well-trained ANN model; and (3) sensitivity analysis using PLCC, identification of the most probabilistic failure point based on FORM, and estimation of the failure probability using the MCS-IS technique. Upon demonstration of its efficiency through analysis of a 12-story frame structure, the method is applied to sensitivity and reliability analysis of the Jiangxinzhou Bridge, a five-span self-anchored suspension bridge, in which both structural parameters and external loads are considered as random variables. The analysis identified a number of structural parameters, as well as external loads, that have a significant influence on structural serviceability and safety. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges. [ABSTRACT FROM AUTHOR]