AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions
The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/158 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548913950064640 |
|---|---|
| author | Leonardo Iacussi Paolo Chiariotti Alfredo Cigada |
| author_facet | Leonardo Iacussi Paolo Chiariotti Alfredo Cigada |
| author_sort | Leonardo Iacussi |
| collection | DOAJ |
| description | The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS sensors integrated into an intelligent IoT infrastructure to predict the bridge deflection behaviour for indirect Bridge Structural Health Monitoring purposes. An experimental setup comprising a bridge model and vehicle equipped with a smart sensing node has been used to generate the dataset. Various models for bridge deflection estimation are deployed on the sensorized vehicle, exploiting edge AI capabilities of smart sensors. This study shows the potential of leveraging data-driven technologies to enhance the performance of low-cost sensors. Additionally, it demonstrates the viability of assessing static deflection shapes of bridges through indirect measurements on board vehicles, underlining the potential of this approach to make SHM more cost-effective and scalable. |
| format | Article |
| id | doaj-art-44d34cf148b8494a93bfe687dde1e8c1 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-44d34cf148b8494a93bfe687dde1e8c12025-01-10T13:21:04ZengMDPI AGSensors1424-82202024-12-0125115810.3390/s25010158AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By ConditionsLeonardo Iacussi0Paolo Chiariotti1Alfredo Cigada2Department of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe la Masa 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe la Masa 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe la Masa 1, 20156 Milano, ItalyThe increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS sensors integrated into an intelligent IoT infrastructure to predict the bridge deflection behaviour for indirect Bridge Structural Health Monitoring purposes. An experimental setup comprising a bridge model and vehicle equipped with a smart sensing node has been used to generate the dataset. Various models for bridge deflection estimation are deployed on the sensorized vehicle, exploiting edge AI capabilities of smart sensors. This study shows the potential of leveraging data-driven technologies to enhance the performance of low-cost sensors. Additionally, it demonstrates the viability of assessing static deflection shapes of bridges through indirect measurements on board vehicles, underlining the potential of this approach to make SHM more cost-effective and scalable.https://www.mdpi.com/1424-8220/25/1/158intelligent sensorsindirect SHMMEMS sensorsedge AIIoT infrastructure |
| spellingShingle | Leonardo Iacussi Paolo Chiariotti Alfredo Cigada AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions Sensors intelligent sensors indirect SHM MEMS sensors edge AI IoT infrastructure |
| title | AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions |
| title_full | AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions |
| title_fullStr | AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions |
| title_full_unstemmed | AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions |
| title_short | AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions |
| title_sort | ai enhanced iot system for assessing bridge deflection in drive by conditions |
| topic | intelligent sensors indirect SHM MEMS sensors edge AI IoT infrastructure |
| url | https://www.mdpi.com/1424-8220/25/1/158 |
| work_keys_str_mv | AT leonardoiacussi aienhancediotsystemforassessingbridgedeflectionindrivebyconditions AT paolochiariotti aienhancediotsystemforassessingbridgedeflectionindrivebyconditions AT alfredocigada aienhancediotsystemforassessingbridgedeflectionindrivebyconditions |