Tunnel boring machines (TBMs) are essential equipment for tunnel excavation. The main component of TBMs for breaking rock is the disc cutter. The effectiveness and productivity of TBM operations are directly impacted by the disc cutter design and performance. This study investigates the effects of confining stress on the breaking force of disc cutters with various diameters. Both saturated and dry rock, such as low-strength concrete, medium-strength marble, and high-strength granite, are used in the tests. It is found that disc cutters with larger diameter can reduce the influence of the confining stress. Moreover, this research indicates that the influence of confining stress is more notable in rocks with higher strengths, especially in dry condition as opposed to saturated condition. The failure load is related to the confining stress, cutter diameter, and compressive strength of the rock in a multivariate linear regression model, suggesting that the confining stress is more significant than the other variables. These results highlight the importance of considering in-situ stress conditions when excavating tunnels by TBMs.

Deep geological repositories are critical for the long-term storage of hazardous materials, where understanding the mechanical behavior of emplacement drifts is essential for safety assurance. This study presents a surrogate modeling approach for the mechanical response of emplacement drifts in rock salt formations, utilizing Gaussian processes (GPs). The surrogate model serves as an efficient substitute for high-fidelity mechanical simulations in many-query scenarios, including time-dependent sensitivity analyses and calibration tasks. By significantly reducing computational demands, this approach facilitates faster design iterations and enhances the interpretation of monitoring data. The findings indicate that only a few key parameters are sufficient to accurately reflect in-situ conditions in complex rock salt models. Identifying these parameters is crucial for ensuring the reliability and safety of deep geological disposal systems.

Structural health monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an “input–output” and an “output-only” method.

The digital twin approach has gained recognition as a promising solution to the challenges faced by the Architecture, Engineering, Construction, Operations, and Management (AECOM) industries. However, its broader application across some AECOM sectors remains limited. A significant obstacle is that traditional DTs rely on deterministic models, which require deterministic input parameters. This limits their accuracy, as they do not account for the substantial uncertainties that are inherent in AECOM projects. These uncertainties are particularly pronounced in geotechnical design and construction. To address this challenge, we propose a probabilistic digital twin (PDT) framework that extends traditional DT methodologies by incorporating uncertainties and is tailored to the requirements of geotechnical design and construction. The PDT framework provides a structured approach to integrating all sources of uncertainty, including aleatoric, data, model, and prediction uncertainties, and propagates them throughout the entire modeling process. To ensure that site-specific conditions are accurately reflected as additional information is obtained, the PDT leverages Bayesian methods for model updating. The effectiveness of the PDT framework is showcased through an application to a highway foundation construction project, demonstrating its potential to integrate existing probabilistic methods to improve decision-making and project outcomes in the face of significant uncertainties. By embedding these methods within the PDT framework, we lower the barriers to practical implementation, making probabilistic approaches more accessible and applicable in real-world engineering workflows.

The assessment of soil–structure interaction (SSI) under dynamic loading conditions remains a challenging task due to the complexities of modeling this system and the interplay of SSI effects, which is also characterized by uncertainties across varying loading scenarios. This field of research encompasses a wide range of engineering structures, including underground tunnels. In this study, a surrogate model based on a regression ensemble model has been developed for real-time assessment of underground tunnels under dynamic loads. The surrogate model utilizes synthetic data generated using Latin hypercube sampling, significantly reducing the required dataset size while maintaining accuracy. The synthetic dataset is constructed using an accurate numerical model that integrates the two-and-a-half-dimensional singular boundary method for modeling wave propagation in the soil with the finite element method for structural modeling. This hybrid approach allows for a precise representation of the dynamic interaction between tunnels and the surrounding soil. The validation and optimization algorithms are evaluated for two problems: underground railway tunnels with circular and rectangular cross-sections, both embedded in a homogenous full-space medium. Both geometrical and material characteristics of the underground tunnel are incorporated into the optimization process. The optimization target is to minimize elastic wave propagation in the surrounding soil. The results demonstrate that the proposed optimization framework, which combines the Bayesian optimization algorithm with surrogate models, effectively explores trade-offs among multiple design parameters. This enables the design of underground railway tunnels that achieve an optimal balance between elastic wave propagation performance, material properties, and geometric constraints.

Thermal integrity profiling (TIP) is a nondestructive testing technique that takes advantage of the concrete heat of hydration (HoH) to detect inclusions during the casting process. This method is becoming more popular due to its ease of application, as it can be used to predict defects in most concrete foundation structures requiring only the monitoring of temperatures. Despite its advantages, challenges remain with regard to data interpretation and analysis, as temperature is only known at discrete points within a given cross-section. This study introduces a novel method for the interpretation of TIP readings using neural networks. Training data are obtained through numerical finite element simulation spanning an extensive range of soil, concrete, and geometrical parameters. The developed algorithm first classifies concrete piles, establishing the presence or absence of defects. This is followed by a regression algorithm that predicts the defect size and its location within the cross-section. In addition, the regression model provides reliable estimates for the reinforcement cage misalignment and concrete hydration parameters. To make these predictions, the proposed methodology only requires temperature data in the form standard in TIP, so it can be seamlessly incorporated within the TIP workflows. This work demonstrates the applicability and robustness of machine learning algorithms in enhancing nondestructive TIP testing of concrete foundations, thereby improving the safety and efficiency of civil engineering projects.

The condition assessment of underground infrastructure (UI) is critical for maintaining the safety, functionality, and longevity of subsurface assets like tunnels and pipelines. This article reviews various data acquisition techniques, comparing their strengths and limitations in UI condition assessment. In collecting structured data, traditional methods like strain gauge can only obtain relatively low volumes of data due to low sampling frequency, manual data collection, and transmission, whereas more advanced and automatic methods like distributed fiber optic sensing can gather relatively larger volumes of data due to automatic data collection, continuous sampling, or comprehensive monitoring. Upon comparison, unstructured data acquisition methods can provide more detailed visual information that complements structured data. Methods like closed-circuit television and unmanned aerial vehicle produce large volumes of data due to their continuous video recording and high-resolution imaging, posing great challenges to data storage, transmission, and processing, while ground penetration radar and infrared thermography produce smaller volumes of image data that are more manageable. The acquisition of large volumes of UI data is the first step in its condition assessment. To enable more efficient, accurate, and reliable assessment, it is recommended to (1) integrate data analytics like artificial intelligence to automate the analysis and interpretation of collected data, (2) to develop robust big data management platforms capable of handling large volumes of data storage, processing and analysis, (3) to couple different data acquisition technologies to leverage the strengths of each technique, and (4) to continuously improve data acquisition methods to ensure efficient and reliable data acquisition.