The seismic performance of a building must be evaluated after it has been affected by an earthquake load. In the evaluation process, ASCE/SEI 41 (2014) requires that the drift of the structure is determined to assess structural performance. This study provides a method that helps engineers in measuring the deflection of reinforced concrete cantilever beams. A deep learning model, called residual networks (ResNets), will be used to classify the deflection based on observation by computer vision. However, determining the optimal values of the hyper-parameters of this model is a challenge. Therefore, a hybrid model that integrates the Jellyfish Search (JS) algorithm and ResNets is developed. JS tunes the hyper-parameters of Stochastic Gradient Decent with Momentum (SGDM), which functions as the ResNets optimizer. The input data that are used to train the model are images that are collected in structural experiments. This experiment involved 29 cantilever beams with various reinforced concrete (RC) designs. These specimen RC beams were tested under simulated seismic loads with lateral displacement control. After each load had been applied to the beam, four single-lens digital cameras captured images from the east, west, north and south. Then, the performance of JS-ResNets was evaluated by comparing its accuracy with that of original ResNets using default hyper-parameters. The results of the analysis show that the proposed model achieves 98.1 percent accuracy, which exceeds that, 96.9 percent, of ResNets. Therefore, the hybrid model can provide insights for use of ResNets in similar visual surveillance tasks.

The seismic performance of a building must be evaluated after it has been affected by an earthquake load. In the evaluation process, ASCE/SEI 41 (2014) requires that the drift of the structure is determined to assess structural performance. This study provides a method that helps engineers in measuring the deflection of reinforced concrete cantilever beams. A deep learning model, called residual networks (ResNets), will be used to classify the deflection based on observation by computer vision. However, determining the optimal values of the hyper-parameters of this model is a challenge. Therefore, a hybrid model that integrates the Jellyfish Search (JS) algorithm and ResNets is developed. JS tunes the hyper-parameters of Stochastic Gradient Decent with Momentum (SGDM), which functions as the ResNets optimizer. The input data that are used to train the model are images that are collected in structural experiments. This experiment involved 29 cantilever beams with various reinforced concrete (RC) designs. These specimen RC beams were tested under simulated seismic loads with lateral displacement control. After each load had been applied to the beam, four single-lens digital cameras captured images from the east, west, north and south. Then, the performance of JS-ResNets was evaluated by comparing its accuracy with that of original ResNets using default hyper-parameters. The results of the analysis show that the proposed model achieves 98.1 percent accuracy, which exceeds that, 96.9 percent, of ResNets. Therefore, the hybrid model can provide insights for use of ResNets in similar visual surveillance tasks.

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