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LPWA Sensor Network for Bridge Structural Monitoring

Solution Overview

Toyama has implemented a comprehensive sensor network infrastructure to monitor critical municipal assets, including bridges in remote mountainous areas.[1] The Toyama City Sensor Network consists of an LPWA (Low Power Wide Area Wireless Technology) network that aggregates information from various sensors, with antennas installed in about 100 public facilities in the city covering 98.9% of the resident population.[1] This infrastructure enables real-time monitoring of structural integrity and automated warning systems for public safety.

Technical Components

The network utilizes LPWA technology to aggregate information from various sensors and includes a platform that manages data collected from IoT sensors.[1] Sensors were placed on a bridge in a mountainous area about 20 km away from the Toyama city centre, where indicator lights installed on-site blink to warn users if an abnormality such as an opening or a break occurs in the structure.[1] An AI-based inspection system was deployed at Yatsuo Ohashi bridge that uses video images to estimate the weights of vehicles traversing the bridge and the resulting structural deflection caused by the vehicles.[2][3] The AI then analyzes the data to estimate the bridge's structural integrity and degradation.[2][3]

Implementation Details

The LPWA network was developed in 2018, with antennas installed in approximately 100 public facilities throughout the city.[1] In FY2019, Toyama started a demonstration experiment that allows private businesses to utilise the sensor network free of charge, with 23 case studies (36 groups) adopted from fields including transportation/people flow, agriculture, remote monitoring/disaster prevention, welfare/nursing care, and performance evaluation/facility management.[1] NTT DOCOMO and Kyoto University deployed the AI-based bridge inspection system on a trial basis at Yatsuo Ohashi bridge in Toyama Prefecture between December 9, 2019 and September 30, 2020.[2][3]

Benefits and Impacts

The bridge monitoring system was demonstrated to lead to labour saving in confirming work needed on bridges, otherwise difficult for city officials to patrol and inspect daily.[1] The automated warning system provides immediate on-site alerts through indicator lights when structural abnormalities are detected, enhancing public safety for bridge users.[1] The AI-based inspection approach enables continuous assessment of structural integrity without requiring manual inspections of remote infrastructure.[2][3]

Climate Adaptation Relevance

This monitoring infrastructure addresses climate-related risks to critical transportation infrastructure in mountainous terrain, where extreme weather events can cause structural damage to bridges. The real-time detection capabilities enable rapid response to structural degradation that may result from increased precipitation, flooding, or other climate-induced stresses on aging infrastructure.

Business Analysis

The demonstration experiment model allows private businesses to utilise the sensor network free of charge, attracting applications from diverse sectors including transportation, agriculture, disaster prevention, welfare, and facility management.[1] This public-private partnership approach enabled 23 case studies involving 36 groups to test applications across multiple industries in FY2019.[1] The collaboration between NTT DOCOMO and Kyoto University for the AI-based bridge inspection system demonstrates how municipal infrastructure monitoring can leverage partnerships with telecommunications providers and academic institutions to deploy advanced technologies.[2][3]

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