Tunnel performance prediction through degradation inspection and digital twin construction

Abstract

Civil infrastructures inevitably undergo a degradation process due to extended periods of operation, frequent service requests, and weathering. This paper proposes a methodology for predicting necessary interventions and presents a database of inspection information for tunnels. The model developed correlates performance loss, measured by the tunnel serviceability index (TSI), to defects detected through a 2D grid composed of classified unit cells converted into a virtual representation. To support the framework developed, intervention time estimates were compared to stochastic degradation models presented in the literature. A limit analysis was performed using inverse exponential equations, utilizing data from different sources and supported by a digital representation of the analysis structure. This was done to estimate times with unitary inspection data, deterioration mapping control, and data connection to the developed inspection model. A railway tunnel for ore transport in northern Brazil was selected as a case study to apply the forecast model, as it showed an accelerated deterioration process within a few years of service. The tunnel was divided into four sections that were analyzed using the methodology developed through inspection-sensitive analysis. The section with the lowest service level (TS 1) matched the geological-geotechnical mapping, which indicated a rock mass with high metamorphic grade and susceptibility to weathering agents. The digital model resulted in an inspection digital twin (IDT) prototype that maps the zones with the greatest problems regarding the degradation process. Metadata is assigned to control elements, and there is online connection to a visual database of the tunnel. The target intervention time was set at 16 years by the developed structure, which falls within all forecast intervals calculated by the limit analyses. The inspection DT enables digital monitoring of the structure under analysis, allowing for efficient and optimized management.