Haiyang Liu

With the advancement of tunnel construction, the load-bearing capacity of shield tunnel segments is diminishing, while issues of deformation and fissuring are becoming more conspicuous, posing direct threats to structural integrity and functionality. Steel fiber reinforced concrete (SFRC) is considered a prevalent material, endowed with high strength, excellent crack control, fracture toughness, and remarkable economic advantages. This paper surveys the state-of-the-art research on SFRC, systematically encapsulating key aspects regarding its composition, attributes, methods of segment reinforcement, constitutive models for SFRC segments, and performance enhancements of SFRC segments. By optimizing steel fiber content, aggregate preparation, and selection of chemical admixtures, the mechanical performance of SFRC can be augmented, among which the aspect ratio (l/d) and volume fraction (V_f) of steel fibers exert the most significant influence. Compared with conventional reinforcing materials, SFRC possesses benefits of low cost, uncomplicated fabrication, and superior durability. As a heterogeneous multiphase composite, SFRC exhibits high strength, stiffness, and excellent crack resistance, which can amplify the load-bearing capacity and deformation resistance of the segments, defer damage inception, and thereby enhance the safety and durability of tunnel-lining segments. This study assists in redressing the deficiencies of current shield tunnel segment reinforcement technologies and further facilitates the extensive employment of SFRC in tunnel segment strengthening and restoration.

An extensive network of tunnels has recently been constructed in the Qinling Mountains. Characterized by high and steep terrain, this network has led to frequent traffic accidents. To address this issue, this paper introduces the theory of resilience into the evaluation system of safety systems during the operation period of highway tunnel groups. Based on this, this paper establishes a resilience evaluation index system for the operation safety system of highway tunnel groups, including a human system, vehicle system, and road system. To address both qualitative and quantitative issues concerning the indicators, this paper employs the analytic hierarchy process (AHP) and entropy weight method to combine and assign weights to the resilience evaluation indicators. Subsequently, the cloud model method is utilized to quantify the level of resilience of the highway tunnel group safety system during the operation period. The study results unveiled the patterns of traffic accidents within the Qinling Tunnel Group from the perspectives of vehicle, road, and human factors. The final weight allocation reveals that the road system has the highest proportion, exerting the greatest influence as a primary level index. Moreover, by taking the Qinling Tunnel Group on the Xihan Expressway as an engineering example, the resilience level of the case project was analyzed and obtained. Proposals for enhancing resilience were put forth, taking into account the project’s unique attributes, encompassing adaptability, resistance, and recovery. Overall, this study validates the feasibility and reliability of the proposed method for assessing the resilience of highway networks, offering empirical support for transportation administrators in the implementation of resilience-enhancing strategies.

The concentration of atmospheric greenhouse gases is being amplified by human activity. Building energy consumption, particularly for heating and cooling purposes, constitutes a significant proportion of overall energy demand. This research aims to establish a smart evaluation model to understand the thermal requirements of building occupants based on an open-access dataset. This model is beneficial for making reasonable adjustments to building thermal management, based on factors such as different regions and building user characteristics. Employing Bayesian-optimized LightGBM and SHAP (SHapley Additive exPlanations) methods, an explainable machine learning model was developed to evaluate the thermal comfort design of buildings in different areas and with different purpose. Our developed LightGBM model exhibited superior evaluation performance on the test set, outperforming other machine learning models, such as XGBoost and SVR (Support Vector Regression). The SHAP method further helps us to understand the interior evaluation mechanism of the model and the interactive effect among input features. An accurate thermal comfort design for buildings based on the evaluation model can benefit the carbon-neutral strategy.