DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGES

DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGE

Optimal design of truss structures for size and shape with frequency constraints using a collaborative optimization strategy

A new metaheuristic strategy is proposed for size and shape optimization problems with frequency constraints. These optimization problems are considered to be highly non-linear and non-convex. The proposed strategy extends the idea of using a single optimization process to a series of collaborative optimization processes. In this study, a modified teaching-learning-based optimization (TLBO), which is a relatively simple algorithm with no intrinsic parameters controlling its performance, is utilized in a collaborative framework and introduced as a higher-level TLBO algorithm called school-based optimization (SBO). SBO considers a school with multiple independent classrooms and multiple teachers with inter-classroom collaboration where teachers are reassigned to classrooms based on their fitness. SBO significantly improves the both exploration and exploitation capabilities of TLBO without increasing the algorithm\u27s complexity. In addition, since the SBO algorithm uses multiple independent classrooms with interchanging teachers, the algorithm is less likely to be influenced by local optima. A parametric study is conducted to investigate the effects of the number of classes and the class size, which are the only parameters of SBO. The SBO algorithm is applied to five benchmark truss optimization problems with frequency constraints and the statistical results are compared to other optimization techniques in the literature. The quality and robustness of the results indicate the efficiency of the proposed SBO algorithm

Deep learning-based visual crack detection using Google Street View images

In this study, the utility of using Google Street View (GSV) for evaluating the quality of pavement is investigated. A convolutional neural network (CNN) is developed to perform image classification on GSV pavement images. Pavement images are extracted from GSV and then divided into smaller image patches to form data sets. Each image patch is visually classified into different categories of pavement cracks based on the standard practice. A comparative study of pavement quality assessment is conducted between the results of the CNN classified image patches obtained from GSV and those from a sophisticated commercial visual inspection company. The result of the comparison indicates the feasibility and effectiveness of using GSV images for pavement evaluation. The trained network is then tested on a new data set. This study shows that the designed CNN helps classify the pavement images into different defined crack categories

School based optimization algorithm for design of steel frames

In this paper, a school-based optimization (SBO) algorithm is applied to the design of steel frames. The objective is to minimize total weight of steel frames subjected to both strength and displacement requirements specified by the American Institute of Steel Construction (AISC) Load Resistance Factor Design (LRFD). SBO is a metaheuristic optimization algorithm inspired by the traditional educational process that operates within a multi-classroom school. SBO is a collaborative optimization strategy, which allows for extensive exploration of the search space and results in high-quality solutions. To investigate the efficiency of SBO algorithm, several popular benchmark frame examples are optimized and the designs are compared to other optimization methods in the literature. Results indicate that SBO can develop superior low-weight frame designs when compared to other optimization methods and improves computational efficiency in solving discrete variable structural optimization problems