Integration of Smart Sensor Networks and Vision-Based Analysis for Realtime Construction Quality Management

Construction sites are inherently hazardous and demand continuous monitoring to ensure both worker safety and structural integrity. Traditional manual inspections and wired sensor systems fall short in providing real-time, comprehensive coverage. This paper presents the design, implementation, and field validation of a low-cost, wireless Structural Health Monitoring (SHM) system integrating Internet of Things (IoT), drone technology, and Artificial Intelligence (AI). The proposed framework utilizes an ESP32 microcontroller paired with MPU6050 Inertial Measurement Units (IMUs), DHT22 temperature sensors, and capacitive moisture sensors. Data is securely streamed via MQTT to the Thinger.io cloud platform. Configured threshold breaches automatically trigger drone deployment (DJI Matrice 300 RTK) for targeted visual inspection, where a fine-tuned VGG16 Convolutional Neural Network achieves 99.2% accuracy in identifying concrete cracks. Field deployment results over 20 days demonstrate 99.8% data transmission reliability and early anomaly detection capabilities, offering significant improvements in safety response and an estimated 50-80% cost reduction over traditional methods.