Many studies have found that High-order neural network (HONN) is enables to boost neural network performance. This research utilize a hybrid model with HONN and Linear Neural Network (NN) concepts to develop high-order and linear neural connectors for layer connections. Consequently, this developed HNN will involve a linear/nonlinear switch for each neural layer connection. Furthermore, fuzzy logic (FL) has already been introduced to neural network and it is also found that the combination of FL and FNN has been proof-reading. Furthermore, fuzzy logic (FL) also has been introduced to neural network and been proofed with fuzzy neural network (FNN). Therefore, this research fuses fuzzy logic additionally to develop a fuzzy hybrid neural network (FHNN) architecture. Sequentially, genetic algorithm (GA) is employed to globally optimize membership function of FL and HNN topology and parameters.
The fundamental of this developed model is a FHNN together with genetic optimization to develop the proposed evolutionary fuzzy hybrid neural networks (EFHNN). EFHNN is capable of handling complexity such as fuzzy/uncertain tasks, linear/nonlinear neural mapping and global optimization. Furthermore, in order to enable to process the EFHNN automatic adaptation, this research work integrates the EFHNN with object-oriented (OO) computer technique which consist of three modules: management module, adaptation module and inference module. The developed system was named as evolutionary fuzzy hybrid neural inference system (EFHNIS).
The main focus of this research will be the optimum linear/nonlinear combinations for neural layers, which is the basis of the proposed hybrid neural network as well as the validation of the proposed EFHNN which collaborated with NN, HONN, FL, and GA concepts. In addition, since the construction managements might have certain issues which are complex and full of uncertain, implementing EFHNN in this topic is proved to be suitable and applicable to reliably assist the decision making in construction industry.

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