Self-sensing cross-tensioned concrete pavements for vehicle load evaluation via use of FBG smart steel strands

• Concrete pavement prestressed with smart steel strands can self-sense vehicle load. • The smart cross-tensioned concrete pavement shows a 100% sensor survival rate and engineering feasibility. • A novel method for identifying dynamic vehicle weight, trajectory, and speed is proposed. • The full-scale field experiment proves the sensing accuracy for vehicle weight and speed can reach 85% and 95%, respectively. • A self-sensing strategy is developed to real-timely detect vehicle parameters based on the time domain moving window. Monitoring the properties of dynamic traffic loads is important for traffic control and highway safety insurance. This research introduces a new type of smart road pavement structure, which employs smart steel strands, embedded with fiber Bragg grating (FBG) sensors, to prestress cross-tensioned concrete pavements (CTCP), forming smart CTCP (SCTCP). In this way, the pavement naturally carries a sensing modality. To prove the engineering feasibility of SCTCP and develop the vehicle load self-sensing modality, in this work, a full-scale SCTCP slab was constructed and field loading tests were conducted. The influence of vehicle load, speed, and trajectory on signal output features were analyzed. An identification scheme is then proposed for sensing vehicle load parameters, which reveals more than 85% and 95% accuracy in terms of wheel weight and speed identification, respectively. Finally, a real-time self-sensing strategy for SCTCP is generated. All vehicle load parameters can be automatically characterized.

Authors

Institutions

Publication Details

Journal
International Journal of Transportation Science and Technology
Published
2026-04-01
DOI
https://doi.org/10.1016/j.ijtst.2025.08.009
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Self-sensing cross-tensioned concrete pavements for vehicle load evaluation via use of FBG smart steel strands

Qifan Zhang, Xuewen Zhu, Ming Huang, Huili Wang et al.
International Journal of Transportation Science and Technology
Structural Health Monitoring Techniques
article

Self-sensing cross-tensioned concrete pavements for vehicle load evaluation via use of FBG smart steel strands

Qifan Zhang, Xuewen Zhu, Ming Huang, Huili Wang, Eleni Chatzi, Runyi Cai
article en

Abstract

• Concrete pavement prestressed with smart steel strands can self-sense vehicle load. • The smart cross-tensioned concrete pavement shows a 100% sensor survival rate and engineering feasibility. • A novel method for identifying dynamic vehicle weight, trajectory, and speed is proposed. • The full-scale field experiment proves the sensing accuracy for vehicle weight and speed can reach 85% and 95%, respectively. • A self-sensing strategy is developed to real-timely detect vehicle parameters based on the time domain moving window. Monitoring the properties of dynamic traffic loads is important for traffic control and highway safety insurance. This research introduces a new type of smart road pavement structure, which employs smart steel strands, embedded with fiber Bragg grating (FBG) sensors, to prestress cross-tensioned concrete pavements (CTCP), forming smart CTCP (SCTCP). In this way, the pavement naturally carries a sensing modality. To prove the engineering feasibility of SCTCP and develop the vehicle load self-sensing modality, in this work, a full-scale SCTCP slab was constructed and field loading tests were conducted. The influence of vehicle load, speed, and trajectory on signal output features were analyzed. An identification scheme is then proposed for sensing vehicle load parameters, which reveals more than 85% and 95% accuracy in terms of wheel weight and speed identification, respectively. Finally, a real-time self-sensing strategy for SCTCP is generated. All vehicle load parameters can be automatically characterized.

International Journal of Transportation Science and Technology
Tongji University (CN), ETH Zurich (CH)
Sustainable cities and communities
Openalex Percentile: Top 7%
Structural Health Monitoring Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.