PSI - Issue 78

Arash Rahimi et al. / Procedia Structural Integrity 78 (2026) 1767–1774

1770

• laser-LDR system To detect vehicle entry and exit, two laser–LDR (Light Dependent Resistor) sensor pairs were installed at both ends of the bridge and connected to an Arduino Mega. This system served not only to detect the presence and motion of vehicles but also to estimate their speed and position by measuring the time delay between entrance and exit signals. A custom Python-based detection algorithm was developed to process the LDR signal transitions exported from the Arduino system. By analyzing the signal drop and rise points, the algorithm accurately identified the number of wheels passing each sensor, detected multi-axle conditions through time delays between wheels, and calculated entry and exit times for speed estimation. This real-time wheel tracking was crucial for aligning vibration data with vehicle load positions and enabled a detailed correlation between vehicle dynamics and sensor responses. • Humidity and Temperature Environmental data were collected using a single SHT31 digital temperature and humidity sensor, also connected to the Arduino Mega. This sensor monitored ambient conditions during the tests, providing temperature and humidity context for vibration response variations. The Arduino Mega handled event-based sensors (LDR and SHT31) and sent data to the same Python system, where synchronized storage and processing were performed. • Vehicle A custom-built, Wi-Fi-controlled Arduino vehicle was used as the moving load for testing. The system featured a web-based user interface (Web UI) for wireless control via smartphone or PC, allowing adjustment of speed and direction. Each car powered by a rechargeable battery and was equipped with one SD card module (to directly store any data from the connected modulus to the car) and one analog accelerometer per car to enable drive-by monitoring. In addition, by using ultrasonic sensor at the front of the car the constant distance sets and when the vehicle reaches the end of the bridge it automatically stops the vehicle and starts to send the data through the Arduino Wi-Fi. Two loading scenarios were tested: 1. A single-car configuration (2 axles), simulating a standard moving load. 2. A dual-car configuration (4 axles), where two cars were mechanically linked using a spring to emulate a longer vehicle. This setup allowed the observation of continuous dynamic effects between front and rear axles. All scenarios were repeated at consistent speeds and along the same path to ensure data reliability and reproducibility.

Fig. 4. Web-based system to control the vehicle