Optical Sensor Network for Structural Load Classification
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Solution Overview
Problem
Current monitoring technologies for detecting and classifying heavy loads on structures, such as bridges, are inadequate in accurately distinguishing superloads and environmental events due to limitations like electromagnetic interference sensitivity, short-distance transmission, and high data storage requirements, especially for large and slow-moving vehicles.
Innovation Solution
A system utilizing a network of optical sensors, including fiber Bragg grating sensors, to measure strain energy and detect heavy loads, which calculates total strain energy and determines whether the load is from a superload vehicle or an environmental event, with a processor and transmitter for alert and report generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromagnetic sensors are used for monitoring structure loading, then measurement capability is provided, but electromagnetic interference sensitivity and short-distance transmission limitations occur
Solution Approach 1:
The patent replaces electromagnetic sensors with optical sensors (fiber optic sensors) that use light instead of electromagnetic fields for sensing. This substitution eliminates sensitivity to electromagnetic interference while maintaining measurement capability through optical strain sensing mechanisms.
Solution Approach 2:
The patent introduces optical fiber as an intermediary medium between the structure and the sensing system. The optical fiber transmits strain information as optical signals, providing isolation from electromagnetic interference and enabling long-distance transmission without signal degradation.
2Measurement precision
If extensive sensor networks are deployed for accurate heavy load detection, then detection accuracy is improved, but data storage requirements and system complexity increase
Solution Approach 1:
The patent extracts and processes strain data locally at distributed sensor nodes or edge computing devices rather than centralizing all raw data. This extraction of processing functions from the central system reduces the total data volume that needs to be stored and transmitted while maintaining detection accuracy.
Solution Approach 2:
The patent segments the sensor network into distributed intelligent nodes that perform local data processing and filtering. Each node independently analyzes its strain measurements and only transmits relevant results, reducing overall data storage requirements while maintaining comprehensive monitoring coverage.
3Reliability
If real-time monitoring of large and slow-moving vehicles is implemented, then superload detection capability is improved, but maintenance needs and system complexity increase
Solution Approach 1:
The patent implements self-diagnostic and self-calibration capabilities within the sensor network nodes. The system automatically detects sensor failures, compensates for drift, and performs maintenance tasks without external intervention, reducing overall system complexity and maintenance burden while improving reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where sensor data is continuously analyzed and used to adjust system parameters, detect anomalies, and trigger maintenance alerts. This closed-loop feedback reduces the need for complex manual monitoring and intervention, simplifying the system while enhancing superload detection reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides robust and accurate detection and classification of heavy loads, including superloads, with reduced maintenance needs and improved structural integrity assessment, enabling timely evaluation of bridge capacity and traffic management.
Implementation Method 1
A system utilizing a network of optical sensors, including fiber Bragg grating sensors, to measure strain energy
Data Source
AI summary
A system includes a sensor network comprising a network of optical sensors coupled to structural members of a structure loaded by vehicles or by an environmental event. A processor is operatively coupled to the sensor network. The processor is configured to receive the strain measurements from the network of optical sensors, calculate total strain energy using the received strain measurements, detect a heavy load on the structure in response to the total strain energy exceeding a total strain energy threshold developed for the structure, and determine whether the heavy load results from a superload vehicle or the environmental event. A transmitter is operatively coupled to the processor and configured to transmit one or both of an alert and a condition assessment report for the structure to a predetermined location in response to determining that the heavy load results from the superload vehicle or the environmental event.