Modern Transportation and Metallurgical Materials

Research on the long‑term service performance maintenance and risk control system for urban underground rail transit facilities

Focusing on the long-term service performance and risk control of subway tunnels， this study investigates influencing factors such as train-induced vibrations， foundation pit excavation， and dewatering associated with external operations. Based on mechanistic research， deformation prediction methods for tunnels under various influencing factors are proposed. Subsequently， a classification standard for tunnel structural safety states is established. Through the analysis of the transverse and longitudinal mechanical performance of shield tunnels， quantitative grading indicators for curvature radius and cross-sectional convergence are defined. By comprehensively considering total deformation and residual deformation margins， a dual-control indicator system integrating “additional deformation” and “cumulative deformation” is introduced. Furthermore， a collaborative control system featuring “active protection + dynamic restoration” is developed， centered on structural deformation monitoring during operational phases. This establishes a structural safety protection mechanism that achieves a dynamic equilibrium between external operation process control and internaltunnel reinforcement. Regarding internal defects， a hierarchical treatment methodology is established， detailing specific remediation pathways.

Special Topic Introduction

Research on expressway anomaly event detection methods based on multimodal large models

A key challenge in expressway anomaly handling is that existing methods usually identify local abnormalities from a single sensing signal， but struggle to jointly determine event type， cause， and risk. To address this issue， this paper proposes a multimodal large-model-based method for expressway anomaly event detection. This paper addresses the demand for intelligent expressway management by proposing a multimodal large-model-based anomaly detection method. It integrates heterogeneous data from multiple sources， including video surveillance， millimeter-wave radar， traffic flow， and me‑teorological information， through a “modality expansion-scenario fine-tuning-cross-modal attention Transformer fusion”approach. This process maps diverse features into the CLIP shared semantic space， enabling unified representation of heterogeneous data. Combining traffic knowledge graphs with prompt engineering guides the large model to deeply analyze the types， causes， and potential risks of abnormal events. Experimental results based on the DAIR-V2X dataset demonstrate that the proposed method significantly outperforms traditional approaches in both detection accuracy and comprehensive semantic understanding. This provides a new technical pathway and theoretical foundation for multidimensional perception on expressways.

Effect mechanism of homogenization treatment on the evolution of dispersoid in Al-Cu-Sc alloy

In order to explore the mechanism by which homogenization processes affect the evolution of dispersed phases in Al-Cu-Sc alloys for automotive engines and identify appropriate heat treatment procedures to improve the comprehensive properties of the alloys, comparative systematic investigations of alloys under various homogenization conditions are carried out via a combination of mechanical property tests and microstructural characterizations. The results indicate that homogenization treatment at 500 ℃ enables the complete dissolution of primary Al_2Cu phases into the Al matrix, yielding a maximum hardness of 79.2 ±0.7 HV. The optimal homogenization process is determined as 500 ℃ × 50 h + 250 ℃ × 1 h. In summary, this homogenization process can optimize the alloy's microstructure and significantly enhance its mechanical and heat-resistant performances, providing theoretical and technical support for the development of novel aluminum alloys for automotive engines and the formulation of corresponding heat treatment processes.

Voiceprint recognition method for security early warning monitoring of rail transit station based on ECAPA‑TDNN model

This paper proposes a voiceprint recognition method for rail transit stations based on the ECAPA⁃TDNN model， and applied this method to security early warning monitoring under complex background noise. To address the limitations of traditional video surveillance in sound event detection，this paper constructs a domain⁃specific dataset comprising 4384 audio samples， including screams， explosion sounds， and environmental noises. An optimization strategy that integrates the Squeeze⁃and⁃Excitation （SE） module for channel attention mechanism with Attentive Statistical Pooling （ASP） is proposed， significantly enhancing the model's robustness in noisy environments. Experiments demonstrate that the module achieves an accuracy of 0.8742 on the test set， with an explosion sound recognition recall rate exceeding 96.17%， validating its practical value in rail transit scenarios.

Voiceprint recognition method for security early warning monitoring of rail transit station based on ECAPA-TDNN model

This paper proposes a voiceprint recognition method for rail transit stations based on the ECAPA-TDNN model, and applied this method to security early warning monitoring under complex background noise. To address the limitations of traditional video surveillance in sound event detection, this paper constructs a domain-specific dataset comprising 4384 audio samples, including screams, explosion sounds, and environmental noises. An optimization strategy that integrates the Squeeze-and-Excitation(SE) module for channel attention mechanism with Attentive Statistical Pooling(ASP) is proposed, significantly enhancing the model's robustness in noisy environments. Experiments demonstrate that the module achieves an accuracy of 0.8742 on the test set, with an explosion sound recognition recall rate exceeding 96.17%, validating its practical value in rail transit scenarios.

Research on hierarchical and categorical assessment method for demand of expressway capacity expansion

With the continuous growth of expressway traffic volume and diversified structures of travel demand， traditional decision-making methods for capacity expansion fail to meet the requirements of refined traffic management. Against the background of expressway construction， this paper proposes a hierarchical and classification analytical framework for capacity expansion demand evaluation. Starting from core traffic indicators including traffic flow， vehicle speed， traffic density and peak-hour characteristics， Principal Component Analysis （PCA） is adopted to extract key factors representing segmentlevel expansion pressure， which eliminates multicollinearity interference among multiple indicators.The K-Means clustering algorithm is further applied to conduct horizontal categorization of road segments， dividing expressway sections into three types： capacity-oriented segments， uneven-flow segments， and segments with no expansion demand. This classification reveals the disparities among road segments in terms of traffic load and peak concentration characteristics. Combined with the Analytic Hierarchy Process （AHP）， a vertical hierarchical grading system is constructed from two dimensions：traffic capacity and flow distribution. Comprehensive evaluation scores are quantitatively calculated，and four expansion demand grades （low， medium-low， medium-high， high） are determined via the quantile method.Empirical verification results demonstrate that the proposed framework can effectively identify road segments with high expansion priority and provide scientific support for resource allocation and traffic governance strategies. Compared with conventional expansion judgment approaches relying on single indicators， the hierarchical classification framework developed in this study can comprehensively reflect the operational pressure and potential bottleneck features of expressways， offering novel insights for expressway planning， capacity expansion and operational management.

Jiangsu Transportation Holding Co., Ltd.'s intelligent capacity expansion empowers main business upgrading: a practical exploration from road network efficiency enhancement to cultivation of new quality productivity

Focusing on the demand for capacity improvement of existing expressway networks and the upgrading of the core business of expressway operators， this paper takes the intelligent capacity expansion practice of Jiangsu Communications Holding Co.， Ltd. as a case study. It reviews the process from section-level pilots to regional coordination and further to the cultivation of new quality productive forces， and analyzes how intelligent capacity expansion supports core business development from the perspectives of technical system construction， business extension and governance improvement. The study finds that， based on an integrated “cloud-network-data-map” technical foundation， AI2 event detection products and a multi-level coordinated control system， Jiangsu Communications Holding Co.,Ltd. has connected the processes of perception， analysis， decision-making and execution， promoting intelligent capacity expansion from local application to network-level coordination. The practice of the “one expressway and three bridges” regional network shows that traffic efficiency increased by about 15%. From 2018 to 2025， the share of non-toll revenue rose from about 15% to 32%， event disposal efficiency improved by about 30%， and the accuracy of traffic control decisions improved by about 40%. The practice indicates that the value of intelligent capacity expansion lies not only in easing congestion and improving traffic capacity， but also in embedding data， algorithms and computing power into the whole process of expressway investment， construction， maintenance， operation， management and service. This helps optimize operation management， service supply and business structure at the same time， and provides a practical reference for state-owned transportation enterprises to cultivate new quality productive forces and upgrade their core business.

From localized intelligence to systemic intelligence: strategy and governance architecture for building a large-scale AI traffic model system for China's expressways

At present, generative artificial intelligence(AI) represented by foundation models is driving a profound transformation in transportation cognition and governance paradigms. As the world's largest large-scale and complex transportation system, China's expressway network can no longer be effectively managed through traditional approaches that rely on localized perception and decentralized decision-making. Meanwhile, the development of transportation foundation models across different regions has shown a fragmented pattern in the absence of a unified top-level framework, posing the risk of creating new “intelligent islands.” This paper argues that China's transportation intelligence development urgently requires a paradigm shift from local intelligence to system intelligence. Based on this perspective, the study systematically elaborates on the strategic urgency, top-level architecture, and implementation pathways for building a nationally unified and coordinated AI foundation model system for transportation. In terms of system design, an integrated closed-loop paradigm of perception –cognition-decision-making-execution is proposed as the core framework, supported by a two-level organizational structure of national coordination and provincial implementation, and a layered capability ecosystem characterized by foundation models for cognition and lightweight models for execution. In terms of practical implementation, the paper further proposes three key pathways, that is, establishing institutional foundations through standards-first governance, driving value realization through scenariooriented applications, and fostering innovation through collaborative ecosystem development. This study aims to provide theoretical guidance for the modernization of China's transportation governance capacity and offer policy references for the implementation of the national strategy of building a transportation powerhouse.

Evolution of microstructure and mechanical properties of high‑nickel austenitic stainless steel microwire during cold drawing

This study systematically investigates the microstructural evolution and mechanical properties of a high nickel austenitic stainless steel microwire subjected to large strain cold drawing. By combining multiple microstructural characterization techniques with mechanical property measurements，the deformation and strengthening mechanisms of the material are elucidated. The results show that the material remains single phase austenitic during the drawing process， and the tensile strength increases significantly from 560 MPa to 2200 MPa. Deformation twinning and dislocation slip are identified as the dominant deformation mechanisms. With an increase of drawing strain， the high density of deformation twin boundaries effectively subdivides the original grains， resulting in the formation of a submicron/nano scale twin lamellar structure. This study provides theoretical guidance for the development of ultra high strength austenitic stainless steel microwires.