Project

# Title Team Members TA Documents Sponsor
43 Digital Twin Bridge Monitoring System
Hanchi Ge
Kowshik Dey
Rongjian Chen
design_document1.pdf
design_document2.pdf
final_paper1.pdf
final_paper2.pdf
proposal2.pdf
video1.mp4
Simon Hu
**Project Advisor:**
Dr. Cristoforo Demartino

**Team Members:**
- Kowshik Arko Dey [arkod2]
- Hanchi Ge [hanchig2]
- Rongjian Chen [rc21]

**Problem:**
Bridges are one of the most vital infrastructures that serve as connectors both inside and outside of a country. They facilitate the movement of people, goods, and vehicles. Despite being marvels of engineering and architecture, accidents in bridges have become more frequent as time passes. The significant causes can be attributed to as being vehicle overloading and structural concerns of the bridge. These type of accidents are more prominent in third world countries, like Bangladesh, where most of the bridges have no monitoring system due to the cost involving these traditional monitoring systems. As a result, the drivers are left to their own assessments and judgements which may lead to accidents and structural damage to the bridges. The development of digital monitoring system can effectively save the money wasted on repetitive maintenance and repair of bridges due to overloading and structural damage.

**Solution Overview:**
The Digital Twin Bridge Monitoring System is designed to address the critical issue of bridge safety and maintenance. This innovative system involves the creation of a digital counterpart for a physical bridge, which is outfitted with advanced pressure sensors. These sensors are crucial for accurately gauging the weight of vehicles as they traverse the bridge, ensuring that the bridge's load capacity is not exceeded. Additionally, the system is equipped with a traffic light mechanism. This feature plays a vital role in warning drivers about potential overloading or existing structural issues, thereby enhancing safety measures.

To demonstrate the practicality and functionality of this system, we plan to construct a scaled-down prototype model. This model will serve as a platform for installing our hardware components, which include various modules such as sensors and a micro-controller. The key to our system's effectiveness lies in the ability to transmit the sensor's processed data to the digital twin platform. This enables the real-time monitoring and detection of the bridge's condition, allowing for immediate responses to any detected problems. Through this advanced monitoring system, we aim to revolutionize how bridge safety is managed, ensuring the longevity and reliability of these critical infrastructures.

**Solution Components:**

*Pressure Sensor Subsystem Overview*
The Sensor Subsystem is strategically designed for early detection of potential risks posed by overweight vehicles. Situated in every lane at the initial incline of the bridge, pressure sensors are meticulously installed. Their primary role is to identify vehicles exceeding the weight limit. Upon detection, the traffic light will show red.

*Displacement Subsystem Overview*
Displacement sensors are used to measure the displacement of a bridge structure, which is critical for us to assess the structural safety of a bridge. Displacement sensors will be strategically placed at key points in the bridge structure, such as supports, beams and joints, to ensure we can fully monitor the health of the bridge.

*Processing Subsystem Details*
The operation of the Processing Subsystem is pivotal for maintaining traffic flow and ensuring safety. Under standard operational circumstances, the traffic signal lights are set to green, allowing vehicles to pass. However, the system is on constant alert for overweight vehicles. The moment an overweight vehicle is identified, the traffic lights switch to flashing red, serving as a clear warning to drivers to halt and not to enter the bridge. Further, should there be any detected structural deformation within the bridge, the signal lights will steadfastly remain red. Moreover, to prevent any approach towards the potentially unsafe bridge, all traffic lights leading to it from the preceding intersection will be deactivated.

The heart of this subsystem is an internal micro-controller responsible for Analog-to-Digital (A/D) conversion and the initial stages of signal processing.

- **Signal Conditioning:** The raw signals from the sensors are often weak and noisy. Signal conditioning modules are used to amplify, filter, and convert these signals into a format suitable for digital processing.
- **Analog-to-Digital Conversion (ADC):** The conditioned analog signals are converted into digital data through ADC. This conversion is essential for the subsequent data analysis and digital twin simulation.
- **Data Processing Unit:** A micro-controller processes the digital data, performing preliminary calculations and data compression to reduce the amount of data to be transmitted.
- **Data Transmission:** The processed data is transmitted to the Digital twin software.

*Power Subsystem Functionality*
It efficiently converts AC power, commonly available from standard outlet sources, into the DC voltages required by the various components of the system. This includes the sensors, micro-controller, and communications modules, thereby guaranteeing their uninterrupted operation and performance.

**Criterion for Success:**
Successful development and integration of the scaled physical model, software, and hardware components. The scale model can use pressure sensors to measure the weight of the passing model car, and when the weight of the car exceeds our preset standard, the signal light of the scale bridge will be red to warn the driver. At the same time, the

signals obtained from the scale model, such as pressure signals and structural deformation signals of the bridge, need to be transmitted in real time to the digital twin monitoring system. Comprehensive documentation of the project's design, implementation, and testing process.

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Seat U: Sensing System for Real-time Library Seat Occupation Detection

Jiayuan Huang, Hangzheng Lin, Jiaqi Lou, Hanyin Shao

Featured Project

# Problem

During the exam week, it is very difficult to find a seat in the library. Sometimes students cannot find a satisfying seat even if they walk through the library all around. Some students complain about unknown traffic in the library. For more convenient library seats seeking, students would like to know which other seats are empty ahead of time in order to decide whether they will go to the library and where to find available seats.

# Solution Overview

We will design a sensor-based device for each table to detect occupancy. The occupancy data will be uploaded through wifi to the cloud. There will be three states for each seat: occupied by people, occupied by items, or unoccupied. Then we will design an APP to visualize these data.

# Components

## The sensing subsystem:

• Data preprocessing and WiFi module to transfer data (ESP32)

• Multi-kinds of sensors to detect objects and collect data

• Wired power supply to support long-term real-time detection

## Human-computer interaction subsystem:

• Database server to store the collected data

• APP on the phone that allows clients to check the status of library seats

• It can indicate whether the seat is occupied with people (reserved by personal items), occupied without people, or available

# Criteria of Success

• Classify three different states of seats (occupied by people, occupied by items, or unoccupied)

• The accuracy of detecting whether a seat is reserved by items is above 90%

• The accuracy of detecting whether a seat is occupied by people is above 95%

• The sensor-based device APP is user-friendly and accurately visualizes the seat occupation

• The states of the seats get updated every 1 minute in the APP

• Adaptive to different kinds of table in the library (flexibility)

• Implement the database server bidirectionally: upload data from the device and download data to the APP