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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 most hospitals, operating rooms are the most costly facilities and consume a large percentage of the hospital recourses. Therefore, an efficient and effective operating room scheduling system is highly demanded for hospitals. In this paper, a SOMO-based approach to solving the operating room scheduling problem is proposed. Computational experiments performed on 100 randomly generated simulations are conducted to test whether the proposed scheduling algorithm can provide appealing arrangements in a reasonable computation time. [Copyright &y& Elsevier]

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

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]