• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Earthquakes and Structures
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• v.4 no.5
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• pp.471-487
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• 2013

Performance of a prototype base isolated building located at Indian Institute of Technology, Guwahati (IITG) has been studied here. Two numbers of three storeyed single bay RCC framed prototype buildings were constructed for experimental purpose at IITG, one supported on conventional isolated footings and the other on a seismic isolation system, consisting of lead plug bearings. Force balance accelerometers and a 12 channel strong motion recorder have been used for recording building response during seismic events. Floor responses from these buildings show amplification for the conventional building while 60 to 70% reduction has been observed for the isolated building. Numerical models of both the buildings have been created in SAP2000 Nonlinear. Infill walls have been modeled as compression struts and have been incorporated into the 3D models using Gap elements. System identification of the recorded data has been carried out using Parametric State Space Modeling (N4SID) and the numerical models have been updated accordingly. The study demonstrates the effectiveness of base isolation systems in controlling seismic response of isolated buildings thereby leading to increased levels of seismic protection. The numerical models calibrated by relatively low level of earthquake shaking provides the starting point for modeling the non-linear response of the building when subjected to strong shaking.