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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]

Construction time matters for activities where rental equipment must be used. The building of a secant pile wall requires the rental of equipment and finding the optimal sequence to minimize the construction time is one way to lower construction costs. In this study we develop an effective and efficient optimization algorithm, which we call self-organizing feature map (SOM)-based optimization (SOMO), to minimize the construction time. The algorithm is applied to a case study to obtain the optimal sequences for both primary and secondary bored piles for a secant pile wall. The new SOMO algorithm is developed based on the ability of the human brain to produce topologically ordered mapping, so as to exploit better solutions via updating the weighting vectors of the neurons in a self-organizing topological way that occurs in the evolution of the feature map for optimization. Given detailed building time of the 16 activities of each bored pile, we find that 143.92 h or 27.21% of the original construction can be saved. The optimal sequences for both primary and secondary bored piles are also determined. The practicability of the SOMO algorithm is substantiated. [ABSTRACT FROM AUTHOR]

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: This paper presents a new approach to incrementally constructing a neural network that is capable of learning new information without forgetting old knowledge. The proposed neural network, called hyper-spherical ARTMAP network (HS-ARTMAP network), is a synthesis of an RBF-network-like module and an ART-like module. The HS-ARTMAP network is trained via a training algorithm similar to the training algorithm for the fuzzy ARTMAP system. To demonstrate the performance of the proposed HS-ARTMAP network, several pattern recognition and function approximation problems were tested. [Copyright &y& Elsevier]

Abstract: A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbitrary environment. In a classifier system, the bucket brigade algorithm is used to solve the problem of credit assignment, which is a critical problem in the field of reinforcement learning. In this paper, we propose a new approach to fuzzy classifier systems and a neuro-fuzzy system referred to as ACSNFIS to implement the proposed fuzzy classifier system. The proposed system is tested by the balancing problem of a cart pole and the back-driving problem of a truck to demonstrate its performance. [Copyright &y& Elsevier]

Abstract: In this paper we propose a clustering algorithm to cluster data with arbitrary shapes without knowing the number of clusters in advance. The proposed algorithm is a two-stage algorithm. In the first stage, a neural network incorporated with an ART-like training algorithm is used to cluster data into a set of multi-dimensional hyperellipsoids. At the second stage, a dendrogram is built to complement the neural network. We then use dendrograms and so-called tables of relative frequency counts to help analysts to pick some trustable clustering results from a lot of different clustering results. Several data sets were tested to demonstrate the performance of the proposed algorithm. [Copyright &y& Elsevier]

Presents a study which proposed an approach to forming feature map algorithm. Goal of the Kohonen self-organizing map algorithm; Simulation results; Conclusion.