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Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners. • The study is to develop a new algorithm, SOM-based Cluster Merging (SOMCM). • The paper is to investigate the relevance for bridge main components and their deterioration types. • The initial result involves 61 clusters being merged to 8 clusters with the predetermined iterations. • The clustering analysis for the final 8 clusters shows 9 major bridge maintenance association rules. [ABSTRACT FROM AUTHOR]

The current deterioration inspection method for bridges heavily depends on human recognition, which is time consuming and subjective. This research adopts Self Organizing Map Optimization (SOMO) integrated with image processing techniques to develop a crack recognition model for bridge inspection. Bridge crack data from 216 images was collected from the database of the Taiwan Bridge Management System (TBMS), which provides detailed information on the condition of bridges. This study selected 40 out of 216 images to be used as training and testing datasets. A case study on the developed model implementation is also conducted in the severely damage Hsichou Bridge in Taiwan. The recognition results achieved high accuracy rates of 89% for crack recognition and 91% for non-crack recognition. This model demonstrates the feasibility of accurate computerized recognition for crack inspection in bridge management. [ABSTRACT FROM AUTHOR]

Abstract: In this study we develop an automatic detection model for discovering erroneous tax reports. The model uses a variety of neural network applications inclusive of the Multi-Layer Perceptrons (MLPs), Learning Vector Quantization (LVQ), decision tree, and Hyper-Rectangular Composite Neural Network (HRCNN) methods. Detailed taxation information from construction companies registered in the northern Taiwan region is sampled, giving a total of 5769 tax reports from 3172 construction companies which make up 35.98% of the top-three-class construction companies. The results confirm that the model yields a better recognition rate for distinguishing erroneous tax reports from the others. The automatic model is thus proven feasible for detecting erroneous tax reports. In addition, we note that the HRCNN yields a correction rate of 78% and, furthermore, generates 248 valuable rules, providing construction practitioners with criteria for preventing the submission of erroneous tax reports. [Copyright &y& Elsevier]

Abstract: Optimization of construction time helps practitioners save time and money. In this study we compare two optimization methods — self organizing map based optimization (SOMO) and particle swarm optimization (PSO) — for minimizing the construction time of a secant pile wall. The comparison is based on data from 207 primary and secondary bored piles for a secant pile wall. The detailed construction time is measured in minutes and broken down into 16 work activities for each unit of the wall which is then used to yield the optimal construction sequence and time. Both optimization methods can be computed quickly but, SOMO yields a more efficient construction sequence with a shorter construction time. The comparison leads to several findings. Particles with randomly selected velocities and positions may lead to convergence at a local optimization. The PSO comparison mechanism for the yielding of optimization limits the process to the winner''s neighbors, which may also converge to a local optimization. The case study shows the superiority of SOMO and provides answers to the dilemmas using the smallest hyper-plane, the entire comparison mechanism to yield the optimization, and the mechanism for weight adjustment between the winner neuron and its neighbors. [Copyright &y& Elsevier]