*Construction quality assessment: A hybrid decision support model using image processing and neural learning for intelligent defects recognition

The term "quality" is defined as the conformance to predetermined requirements or specifications. These requirements may be simple or complex. They may be set in terms of the end result required or as a detailed description of how work should be executed. Recently, there has been increasing interest in quality assurance in the construction industry. Quality assurance includes design and planning, sampling, inspection, testing, and assessment to ensure that end products perform according to specifications. This research proposes a new quality assessment model for civil infrastructure and constructed facilities and more specifically for surfaces quality assessment. The research proposes a hybrid decision support model using image processing and neural networks for defect recognition and measurement. The basic concept of the model is to acquire digital images of the areas to be assessed and analyze those images to recognize and measure defect patterns. Neural networks are incorporated into the model to learn from example and simulate human expertise to automate the process for future use. The principles of this model can be applied in many construction quality assessment applications such as steel bridges surfaces, sewer lines, etc. The application of steel bridge coating assessment is used to exemplify the model by applying the principles to realistic quality assessment scenarios. The model is supplemented with a statistical quality assessment plan to use the model efficiently and obtain consistent and reliable results. The statistical plan will determine the number and locations of assessment images to be taken. Moreover, the plan will address the risks associated with the estimated assessment. Finally, the plan will assist making the final acceptance/rejection decision based on the predefined criteria for acceptance and rejection.