Abstract:
Construction noise is one of the most common occupational hazards in the construction industry. It can cause permanent hearing loss, tinnitus, and other health problems. In this thesis, propose a deep learning approach for harmful construction noise identification to provide a solution to this problem. The proposed system is a web application that can identify construction noises and classify them into different noise categories. The system is designed using convolutional neural networks (CNNs), a popular deep-learning technique for sound classification. The proposed system was trained and evaluated using a dataset of construction noises. The dataset was preprocessed and transformed into spectrograms using the Short-Time Fourier Transform (STFT) technique. The CNN model was trained on the transformed dataset and achieved a classification accuracy of over 73%. The proposed system has significant implications for the construction industry as it provides a cost-effective solution for identifying and monitoring harmful construction noises. The system can be used by safety managers, workers, and policymakers to promote a safer and healthier work environment. The results of this research demonstrate the potential of deep learning approaches for solving occupational safety and health problems in the construction industry.
