No abstract

This paper proposes a novel technique to estimate site-specific characteristic maximum traffic load effects on the Forth Road suspension Bridge (FRB). Congestion represents a critical loading state for long-span bridges and congested traffic data such as car/truck mix, vehicle weights and inter-vehicle gaps are not generally simultaneously available. Most Weigh-in-Motion (WIM) systems used worldwide to collect traffic data are not reliable in congested conditions. A closed-circuit television camera and image processing techniques were used here to collect five months of bridge traffic data on the FRB. Frames extracted from videos are automatically analysed by applying standard image processing algorithms. Hence vehicle lengths and relative positions on the bridge are extracted. An on-site WIM database with free-flowing traffic data is used to establish a statistical correlation between vehicle lengths and their weights. This correlation is applied to infer weights from the vehicle lengths recorded on the bridge. Congestion is modelled for northbound traffic by collapsing the inter-vehicle gaps to minimum values. The axial force in the main cable of the bridge is calculated using the corresponding influence line. The characteristic maximum values for axial force are evaluated for 75-year and 1000-year return periods.

In this paper, a field study is carried out to monitor the natural frequencies of Malahide viaduct bridge which is located in the north of Dublin. The bridge includes a series of simply supported spans, two of which collapsed in 2009 and were replaced. The replaced spans are stiffer than most of the others and these differences resulted in higher natural frequencies. An indirect bridge monitoring approach is employed in which acceleration responses from an instrumented train are used to estimate the natural frequencies of each span of the viaduct showing the locations of the two replaced spans with higher stiffness. For the indirect approach, an Ensemble Empirical Mode Decomposition (EEMD)-based Hilbert Huang Transform (HHT) technique is employed to identify the natural frequency of each span. This is carried out by analysing the Instantaneous Frequencies (IFs) from the calculated intrinsic mode functions. The average of the IFs calculated using 41 runs of the instrumented train (with varying carriage mass and speed for each run) are used to estimate the natural frequencies. To assess the feasibility of the indirect approach, a bespoke set of direct measurements was taken using accelerometers attached successively on each span of the viaduct. The free and forced vibrations from each span are used to estimate the first natural frequencies. The frequencies obtained from drive-by measurements are compared to those from direct measurements which confirms the effectiveness of indirect approaches. In addition, the instantaneous amplitudes of the drive-by signals are used to indicate the location of the stiffer spans. Finally, the accuracy and robustness of the indirect approaches for monitoring of multi span bridges are discussed.

This paper proposes an algorithm for the estimation of extreme intensity of traffic load on long-span bridges. Most Weigh-in-Motion technologies do not operate in congested conditions which are the governing cases for these bridges. In the absence of Weigh-in-Motion data on the bridge itself, a correlation between vehicle weights and their lengths is established here using a (freeflowing) Weigh-in-Motion database. Photographic images of congested traffic are modelled here for three bridges using weights estimated from lengths and one year of Weigh-in-Motion data. The actual weights are taken from the Weigh-in-Motion data, and the results are compared to test the method. The gaps between vehicles are firstly set to a constant value and later to Beta-distributed values according to vehicle type. The intensity of traffic load for all pictures is calculated and compared to the loads obtained from the recorded weights. A return period of 75-year is chosen to evaluate the extreme values of intensity. The probability that intensity of load is being exceeded is obtained using normal probability paper for both recorded and simulated weights. This study demonstrates the feasibility of the proposed concept of using lengths to estimate the extreme traffic load events with acceptable accuracy.