The aim of this study is to be able to come up with a supplemental project management policy guidelines and computational tool that will address the two major concerns in construction of low-cost housing, construction delays and workmanship defects. Through assessment of previous studies, factors causing delays and defects from the two major stakeholders involved in housing development projects were identified. With the use of the five-point Likert Scale in survey forms distributed to 60 professionals involved in housing development projects, factors were classified and identified according to its degree of impact on the overall construction efficiency. The statistics of these factors were organized and used to develop an Artificial Neural Network Model. The relative importance of the factors was measured using Garson’s Algorithm. The derived equations from the developed ANN Model were used in formulating the computational tool and supplemental policy guidelines that can now be used to evaluate the workmanship defects and delay ratings of different housing developments. The computational tool was tested by 10 experts with their current projects and was able to receive a 4.6 out of 5 rubric evaluation rating, showing the tool’s effectiveness in identifying and assessing the probability and impact of construction deficiencies on their projects.
Applying an Artificial Neural Network to Predicting Effort and Errors for Embedded Software Development Projects
