Fiber is effective in restricting cracks and improving the toughness of geopolymer composites, but few studies have focused on the surface crack characteristics of fiber-reinforced geopolymer composites. In this paper, after flexural tests of polypropylene fiber-reinforced geopolymer mortar, the surface cracking image was collected by a digital camera and cracking information was extract by deep learning. Finally, the cracking and fractal characteristics were specifically discussed. The semantic segmentation network can accurately extract surface cracks for calculating various parameters. The results showed that the mean intersection over union (mIoU) and mean pixel accuracy (mPA) of the cracks are 0.8451 and 0.9213, respectively. Generally, the crack length, width, area, and fractal dimension of the specimen are all increased with the increase in the fiber volume fraction. These crack parameters grow rapidly when the fiber content is small, and the growth of the crack parameters gradually slows down as the fiber volume fraction increases to approximately 1.5%. The highest crack parameter values were found in the geopolymer mortar, with a 0.48 water–binder ratio and 12 mm fiber length. The variation of the bottom crack length and the side crack fractal dimension can be used to represent the overall crack variation patterns. Meanwhile, the crack parameters increase with the increased fiber factor in a quadratic function. Based on these crack parameters, the critical fiber factor and dense fiber factor of polypropylene fiber-reinforced geopolymer mortar were 200 and 550, respectively. They are greater than those of fiber-reinforced Portland cementitious composites. The influence of various crack parameters on the flexural strength is in the order of the crack area, width, length, and fractal dimension.

A continuous and smooth hysteretic model for shear-critical reinforced concrete (RC) columns was identified and calibrated based on an efficient stochastic search technique and an experimental database consisting of 30 shear-critical RC columns. The inelastic restoring force of shear-critical columns was described by the Bouc-Wen-Baber-Noori (BWBN) model to produce the requisite strength and stiffness degradation as well as pinching effect. Meanwhile, the backward Euler and Newton-Raphson schemes were adopted to determine the inelastic restoring force by solving the ordinary differential equations. Then a differential evolution (DE) algorithm was utilized to identify the control parameters of the BWBN hysteretic model based on an experimental database consisting of 30 shear-critical rectangular columns under cyclic loading. Finally, prediction equations for the BWBN model parameters were developed in terms of four structural physical parameters. The capability and accuracy of the calibrated hysteretic model were demonstrated by comparison with the available experimental data.

To deal extensively with issues on implicit performance function and huge computational cost in reliability analysis, a new method for structural reliability analysis was proposed by combining the GP and importance sampling method (ISM). Firstly, a small amount of training dataset is generated by the structural analysis to train the GP. Then, the implicit performance and its derivatives are approximated by the trained GP using explicit formulations. Secondly, an iterative algorithm called as the GP-based first-order reliability method is implemented to obtain the design point. During iterations, the precision of the GP approximation in the important region, which contributes significantly to the failure probability, is improved continuously by adding the new iterative design point into the training set. Finally, an importance sampling around the design point is applied to obtain the failure probability. To assess the validity of the proposed method, five numerical examples were presented and discussed, which validated that accurate and computationally efficient results for structural reliability analysis can be obtained using the proposed method.