Routine inspection and automatic distress detection and classification are critical for civil infrastructures such as bridges. The main subject of this paper is to provide brief review a multi-agent fuzzy system (MAFs) based on image analysis for the detection and classification of, various types of cracking in concrete elements. For this purpose, the combination of fuzzy inference systems has been developed as an autonomous intelligent agent in the center of a multi-agent system (MAS) which communicate, and exchange information with each other. This work is presented in two main sections, (1) the crack recognition system and (2) the type detection system that both of them designed based on MAS fuzzy systems. The first module is a binary classification agent that made of 5 inputs, one output, 11 rules. This agent receives an image and classified it into two groups: crack and non-crack. The second module, which is made of 8 inputs, three output, 20 rules, and used for type classification (individual, pattern, and random). The input of this module is the images that were classified using the first module into the crack group. Particle swarm optimization has been used to find the optimal values of membership functions coefficients. The optimized results of multi-agent modules are compared with other methods. After an experimental characterization and optimization of modules, the MAFs are tested on various concrete distresses. The results show a high potential of MAFs for crack detection and classification. Analysis of the results showed that accuracy of detection, classification can be improved by 4% and 5%, respectively, with MAFs. This method enhances the speed, accuracy, and has higher precision, which indicates the satisfaction and reliability of the MAFs. Also, besides this system has high computational power to detect and classify complex cracking patterns in bridge components.

例行检查以及自动遇险检测和分类对于诸如桥梁之类的民用基础设施至关重要。本文的主要主题是简要概述基于图像分析的多主体模糊系统（MAF），以检测和分类混凝土构件中各种类型的裂缝。为此，已经将模糊推理系统的组合开发为位于多主体系统（MAS）中心的自治智能主体，该主体相互通信并交换信息。这项工作分为两个主要部分，（1）裂纹识别系统和（2）两者都是基于MAS模糊系统设计的类型检测系统。第一个模块是一个二进制分类代理，由5个输入，一个输出，11个规则组成。该代理接收图像并将其分为两类：裂纹和非裂纹。第二个模块由8个输入，三个输出，20个规则组成，用于类型分类（单个，模式和随机）。该模块的输入是使用第一个模块分类为裂纹组的图像。粒子群算法已被用于寻找隶属函数系数的最优值。将多代理模块的优化结果与其他方法进行比较。在对模块进行实验性表征和优化之后，对MAF进行了各种混凝土遇险测试。结果表明，MAF在裂纹检测和分类方面具有很高的潜力。结果分析表明，使用MAF分别可以提高4％和5％的检测精度。该方法提高了速度，准确性，并具有较高的精度，表明了MAF的满意度和可靠性。此外，该系统还具有很高的计算能力，可以检测和分类桥梁构件中的复杂裂缝模式。