Sensitivity analysis of neural networks in spool fabrication productivity studies

The back-propagation neural network (BPNN) has been researched and applied as a convenient decision-support toot in a variety of application areas in civil engineering. However, learning algorithms such as the BPNN do not give information on the effect of each input parameter or influencing variable upon the predicted output variable. The model's sensitivity to changes in its parameters is generally probed by testing the response of a mature network on various input scenarios. In this paper, the relationships between an output variable and an input parameter are sorted out based on the BPNN algorithm. The input sensitivity of the BPNN is defined in exact mathematical terms in light of both normalized and raw data. The difference between a BPNN and regression analysis of statistics is discussed, and the sophistication and superiority of the BPNN over regression analysis is further demonstrated in a case study based on a small data set. In addition, statistical analysis of input sensitivity based on Monte Carlo simulation enables the modeler to understand the rationale of a BPNN's reasoning and have preknowledge about the effectiveness of model implementation in a probabilistic fashion. The sensitivity analysis of the BPNN is successfully applied to analyze the labor production rate of pipe spool fabrication in a real industrial setting. Important aspects of the application, including problem definition, factor identification, data collection, and model testing based on real data, are discussed and presented.