Carbon emissions have become a focus of political and academic concern in the global community since the launch of the Kyoto Protocol. As the largest carbon emitter, China has committed to reaching the carbon peak by 2030 and carbon neutrality by 2060 in the 75th United Nations High-level Meeting. The transport sector needs to be deeply decarbonized in China to achieve this goal. Previous studies have shown that the carbon emissions of the railway sector are small compared to highways, waterways, and civil aviation. However, these studies only consider the operation stage and do not consider the carbon emissions caused by large-scale railway infrastructure construction during the construction stage. As an essential source of carbon emissions and the focus of emissions reduction, the carbon emission of railway construction projects (RCPs) is in urgent need of relevant research. Based on a systematic literature review (SLR), this paper sorts out the carbon emission factors (CEFs) related to RCPs; combines semi-structured expert interviews to clarify the carbon emissions measurement boundary of RCPs; modifies and calibrates CEFs; constructs the carbon emission measurement model of RCPs including building material production stage, building material transportation stage, and construction stage; and conducts empirical analysis to validate carbon emission factors and measurement models. This study effectively complements the theoretical research on CEFs and measurement models in the construction stage of railway engineering and contributes to guiding the construction of low-carbon railways practically.

Introduction: Falls from height (FFH) accidents can devastate families and individuals. Currently, the best way to prevent falls from heights is to wear personal protective equipment (PPE). However, traditional manual checking methods for safety hazards are inefficient and difficult to detect and eliminate potential risks.Methods: To better detect whether a person working at height is wearing PPE or not, this paper first applies field research and Python crawling techniques to create a dataset of people working at height, extends the dataset to 10,000 images through data enhancement (brightness, rotation, blurring, and Moica), and categorizes the dataset into a training set, a validation set, and a test set according to the ratio of 7:2:1. In this study, three improved YOLOv5s models are proposed for detecting PPE in construction sites with many open-air operations, complex construction scenarios, and frequent personnel changes. Among them, YOLOv5s-gnconv is wholly based on the convolutional structure, which achieves effective modeling of higher-order spatial interactions through gated convolution (gnConv) and cyclic design, improves the performance of the algorithm, and increases the expressiveness of the model while reducing the network parameters.Results: Experimental results show that YOLOv5s-gnconv outperforms the official model YOLOv5s by 5.01%, 4.72%, and 4.26% in precision, recall, and mAP_0.5, respectively. It better ensures the safety of workers working at height.Discussion: To deploy the YOLOv5s-gnConv model in a construction site environment and to effectively monitor and manage the safety of workers at height, we also discuss the impacts and potential limitations of lighting conditions, camera angles, and worker movement patterns.

High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework’s capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects.

Construction workers’ unsafe behaviors (CWUBs) are a primary cause of construction safety accidents in railway tunnel engineering (RTE). Understanding the causation mechanism between construction safety accidents, CWUBs, and their influencing factors is crucial for improving safety management. However, research in this area remains insufficient. This study systematically identifies 9 types of construction safety accidents, 11 types of CWUBs, and 35 influencing factors, covering three core dimensions: organizational management, individual safety capacity, and safety environment. Using the 24model, this study qualitatively elucidates the causation mechanism and identifies the primary and secondary causation relationships among 55 factors. On this basis, a network model of CWUBs in RTE is developed and quantitatively analyzed using social network analysis from the perspectives of the overall network, block network, and individual network, resulting in the identification of a critical network comprising 27 key factors. Based on the findings, nine targeted intervention measures are proposed, encompassing pre-emptive prevention, on-site control, and emergency management. This study innovatively integrates the 24model and social network analysis, systematically analyzing the causation mechanism of CWUBs in RTE from both qualitative and quantitative perspectives. This research not only provides a systematic and innovative analytical framework for CWUBs in RTE, addressing a critical gap in the study of unsafe behaviors and accident causation in complex systems, but also offers practical guidance for safety risk management. Additionally, it enriches the theoretical framework of unsafe behavior research, providing valuable insights for further studies in related fields.

With China’s government facilitating railway projects, more railway bridges (RBs) are gradually built in complex and difficult mountainous areas (CDMAs). The construction activities of RB in CMDAs are facing formidable challenges in terms of the natural environment, technology, and organization management, which are hardly solved by traditional bridge design and construction techniques. Therefore, there is an urgent need for technology innovation (TI). Studies on the management of RB-TI in CMDAs are limited. As such, this study aims to offer an effective modular management approach to RB-TI in CMDAs. A system of demand and obstacle factors for RB-TI in CMDAs was identified firstly based on the literature review and the grounded theory, including seven intermediate codings and 29 initial codings. Then these factors were regarded as the system requirements for modular decomposition, to establish a “cut-to-fit” modular management approach to RB-TI in CMDAs. A case (i.e., the LD bridge project of the CZ railway) was selected to demonstrate and validate the developed approach. The results show that the proposed approach can be applied to manage RB-TI in CMDAs. The innovation of this study lies in the integration of grounded theory and modular theory and provides modular management ideas and measures for bridge engineering technology innovation. Findings from this study enrich the knowledge body of RB-TI and guide innovation subjects in the practical management of RB-TI in CMDAs.