Life Prediction of Underground Concrete Structures: From Mechanism-Based Models to Digital Twin Frameworks

Underground concrete structures are exposed to a multi-ion groundwater and seepage–leakage coupling environment for a long time, and it is difficult to observe visually, which makes it difficult to accurately characterize important boundary conditions and defect states, resulting in significant time-varying and spatially differing characteristics of the concrete deterioration process. Therefore, its durability assessment and life prediction are significantly different from those of above-ground structures. Aiming at the complex prediction problem of limited service information of underground concrete, this paper summarizes and combs the evolution process of underground concrete life prediction methods, and puts forward the evolution process of five generation prediction frameworks: from a deterministic mechanism model (Gen-1) to a multi-physical field coupling model (Gen-2), a probabilistic reliability framework (Gen-3), a data-driven and physical information fusion method (Gen-4) and then to a digital twin framework for online update and system integration (Gen-5). Differently from the traditional review by model category, this paper reveals the internal logic of life prediction from single life point values to time-varying risk assessment from the perspective of the transformation of prediction targets and problem structures. Based on the comparison of typical underground service environments, it is further shown that the key constraints of prediction ability are usually derived from insufficient observability and limited parameter identifiability, as well as model structure errors introduced by deterioration mechanism switching and local defects, rather than physical model complexity. On this basis, this paper proposes the selection idea of life prediction methods for different underground scenes, emphasizing measurable characterization, hierarchical verification and hierarchical calculation as the core, and effectively connecting the mechanism model, uncertainty analysis, data update and operation and maintenance decisions. In this paper, the life prediction of underground concrete is redefined as a dynamic evaluation process embedded in the whole life management of infrastructure, which provides a theoretical framework and research direction for the construction of a reliable and deployable life prediction system of underground concrete.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2026-05-22
DOI
https://doi.org/10.3390/buildings16112047
Primary Topic
Concrete Corrosion and Durability
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Life Prediction of Underground Concrete Structures: From Mechanism-Based Models to Digital Twin Frameworks

Yuxia Li, Hui Lin, Xiongfei Liu, Bin Yang et al.
Buildings
Concrete Corrosion and Durability
article

Life Prediction of Underground Concrete Structures: From Mechanism-Based Models to Digital Twin Frameworks

Yuxia Li, Hui Lin, Xiongfei Liu, Bin Yang, Jianglin Liu
article en

Abstract

Underground concrete structures are exposed to a multi-ion groundwater and seepage–leakage coupling environment for a long time, and it is difficult to observe visually, which makes it difficult to accurately characterize important boundary conditions and defect states, resulting in significant time-varying and spatially differing characteristics of the concrete deterioration process. Therefore, its durability assessment and life prediction are significantly different from those of above-ground structures. Aiming at the complex prediction problem of limited service information of underground concrete, this paper summarizes and combs the evolution process of underground concrete life prediction methods, and puts forward the evolution process of five generation prediction frameworks: from a deterministic mechanism model (Gen-1) to a multi-physical field coupling model (Gen-2), a probabilistic reliability framework (Gen-3), a data-driven and physical information fusion method (Gen-4) and then to a digital twin framework for online update and system integration (Gen-5). Differently from the traditional review by model category, this paper reveals the internal logic of life prediction from single life point values to time-varying risk assessment from the perspective of the transformation of prediction targets and problem structures. Based on the comparison of typical underground service environments, it is further shown that the key constraints of prediction ability are usually derived from insufficient observability and limited parameter identifiability, as well as model structure errors introduced by deterioration mechanism switching and local defects, rather than physical model complexity. On this basis, this paper proposes the selection idea of life prediction methods for different underground scenes, emphasizing measurable characterization, hierarchical verification and hierarchical calculation as the core, and effectively connecting the mechanism model, uncertainty analysis, data update and operation and maintenance decisions. In this paper, the life prediction of underground concrete is redefined as a dynamic evaluation process embedded in the whole life management of infrastructure, which provides a theoretical framework and research direction for the construction of a reliable and deployable life prediction system of underground concrete.

BuildingsVol. 16(11)
Hebei University of Technology (CN), Beijing Forestry University (CN), Beijing University of Technology (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
Concrete Corrosion and Durability
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.