GASPRO International Journal of Eminent Scholars

ABSTRACT

This study examined the potency of artificial intelligence in wall crack detection and sustainable remedies against future structural damage. Descriptive survey design was adopted to carry out this research in South-South, Nigeria. The targeted population for the study comprised all architect in South-South, Nigeria. Stratified sampling technique was used to select 40 licensed architects from each of the three states in South- South (Akwa-Ibom, Rivers and Cross-River) this gave a sample size of 120 respondents used to carry out this research. The instrument used for data collection was a structured questionnaire titled “Artificial Intelligence, Wall Crack Detection and Sustainable Remedies Questionnaire (AIWCDSRQ)”. Face and content validation of the instrument was carried out by an expert in test, measurement, and evaluation in order to ensure that the instrument has the accuracy, appropriateness, and completeness for the study under consideration. The reliability coefficient obtained was 0.93, and this was high enough to justify the use of the instrument. The researcher subjected the data generated for this study to appropriate statistical technique such as descriptive statistics to answer research questions and regression analysis to test the hypothesis. The result shows that computer vision was the highest use of artificial intelligence in wall crack detection. It was also observed that computer vision and image processing was rated as the highest use of Artificial Intelligence in preventing wall crack. It also revealed that AI has a significant impact on the mitigation of future structural damage of buildings through wall crack detection and sustainable remedies. The study concluded that the application of artificial intelligence in wall crack detection marks a pivotal advancement in structural health monitoring. One of the recommendations made was that governments and construction firms should incorporate AI with real-time monitoring tools such as IoT sensors and drones to continuously assess structural health.

KEYWORDS: Artificial Intelligence, Crack Detection, Sustainable Remedies and Structural Damage