Lifecycle Management of Bridge and Tunnel Infrastructure Using Digital Twin Technology

Digital twins are helping control infrastructure assets, from bridges to tunnels, with live, data-driven digital clones that replicate and adapt to their originals. In this paper, we describe the role of digital twins in bridge and tunnel lifecycle management, monitoring, predictive maintenance and productivity. The solution leverages IoT sensors, cloud computing, and machine learning algorithms to provide real-time monitoring, dynamic analysis and predictive insight. This allows for proactive decision making to allow for structural problems and risk mitigation in advance. Major problems such as the high initial costs, unstandardizedness, and interoperability are discussed alongside new solutions like improvements in sensors and cooperative frameworks. Specifically, future research fields such as AI, edge computing, and blockchain are also discussed. Case studies and analysis highlight how digital twins can help to refocus resources, extend the lifecycle of infrastructure, and achieve sustainability objectives, making them a valuable asset for todays infrastructure management.

Authors

Institutions

Publication Details

Journal
Applied and Computational Engineering
Published
2025-04-03
DOI
https://doi.org/10.54254/2755-2721/2025.21784
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
1.24
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lifecycle Management of Bridge and Tunnel Infrastructure Using Digital Twin Technology

Kuan Wang
1 citations
Applied and Computational Engineering
Manufacturing Process and Optimization
1.24
article

Lifecycle Management of Bridge and Tunnel Infrastructure Using Digital Twin Technology

Kuan Wang
article en
1 citations

Abstract

Digital twins are helping control infrastructure assets, from bridges to tunnels, with live, data-driven digital clones that replicate and adapt to their originals. In this paper, we describe the role of digital twins in bridge and tunnel lifecycle management, monitoring, predictive maintenance and productivity. The solution leverages IoT sensors, cloud computing, and machine learning algorithms to provide real-time monitoring, dynamic analysis and predictive insight. This allows for proactive decision making to allow for structural problems and risk mitigation in advance. Major problems such as the high initial costs, unstandardizedness, and interoperability are discussed alongside new solutions like improvements in sensors and cooperative frameworks. Specifically, future research fields such as AI, edge computing, and blockchain are also discussed. Case studies and analysis highlight how digital twins can help to refocus resources, extend the lifecycle of infrastructure, and achieve sustainability objectives, making them a valuable asset for todays infrastructure management.

Applied and Computational EngineeringVol. 140(1)
Wuhan University of Technology (CN)
Openalex Percentile: Top 21%
Manufacturing Process and Optimization
1.24
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.

Lifecycle Management of Bridge and Tunnel Infrastructure Using Digital Twin Technology — Kuan Wang · Applied and Computational Engineering (2025) | TGRS Research Map | TGRS