# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### Time Series Sensory Data Collection and Processing  
#### 4.3.1 Sensor Deployment and Data Acquisition  
In BDT modeling, an effective sensor network is essential for acquiring high-quality data that accurately reflect the condition of the bridge [43]. Each type of sensor plays a distinct role in capturing specific aspects of the structure's behavior, and their placement must be carefully planned to ensure comprehensive monitoring.  
Accelerometers are used to capture the dynamic responses of the bridge, particularly vibrations induced by traffic loads, wind, and environmental forces [44]. These sensors are typically installed on the main girders, deck, and critical load-bearing elements. Their placement is designed to monitor vibrations across various sections of the structure, providing a comprehensive view of the bridge's dynamic behavior. The data collected by accelerometers are essential for identifying modal parameters, such as natural frequencies and mode shapes, which help assess the structural integrity and detect abnormal vibrations that may indicate damage or degradation.  
Strain gauges are installed at key structural locations, such as piers, abutments, and support beams, where significant stress is expected. These sensors continuously monitor the strain in the bridge components, allowing engineers to measure how the structure responds to loads over time. The data collected reflect the bridge's deformation under varying loads and provide insights into localized stress concentrations, which are critical for identifying potential fatigue and material failure. Strain data are fundamental for long-term health assessments, helping to detect early signs of structural weakening.  
Temperature sensors are distributed across the bridge to monitor environmental and material temperatures, particularly in areas prone to significant thermal expansion and contraction [45]. These sensors are often placed on the deck, beams, and supports where temperature-induced stress may occur. The data collected are essential for adjusting other sensor measurements, such as strain and displacement, as temperature variations directly affect these readings. Accurate temperature data are crucial for distinguishing between temperature-driven structural changes and true material deformations, ensuring the reliability of time series analysis.  
Global Navigation Satellite System (GNSS) sensors are placed on the bridge's key components, such as the deck and towers, to measure displacements and monitor large-scale deformations [46]. These sensors are particularly useful for tracking movements due to settling, shifting foundations, or long-term environmental effects. The GNSS data enable the monitoring of slow, cumulative deformations that can impact the stability of the bridge over time. By providing precise measurements of large-scale shifts, GNSS sensors are essential for evaluating overall structural stability.  
The data collected from these sensors are time-stamped and synchronized to form comprehensive time series datasets. These datasets undergo further preprocessing to denoise and standardize the data for analysis. Once preprocessed, the data are suitable for advanced time series modeling, such as long short-term memory (LSTM) analysis, which is used to predict structural changes and potential failure points based on historical trends, as shown in Figure 7.