With the increasing computing capacity of intelligent connected vehicles (ICVs), there will be a considerable amount of spare resources available in the vehicle cluster. It is necessary to make full use of these spare resources. In this paper, we investigate a platoon intelligence-based edge learning (PIEL) framework for the deep integration of terminal and network edge. Specifically, this paper discusses the characteristics and applications of platooning in edge learning and introduces the general architecture of PIEL. Subsequently, we conduct a discussion on the learning scheduling based on PIEL, including co-scheduling in the Intra-Platoon, Inter-Platoon, and Platoon-edge cases. An intelligent decision-making methodology framework for PIEL is also presented. Finally, we discuss future research opportunities on PIEL to achieve platoon intelligence. We believe that this investigation will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on edge intelligence.

Rock burst is a difficult and urgent problem during the construction process of underground engineering in a zone with high surrounding rock pressure. Pressure relief blasting is one of the most effective methods for solving the problem. An in-situ blasting test was carried out in a tunnel under excavation, and the geology stress (geo-stress) of the surrounding rock was tested before and after the pressure relief blasting. The experimental results will be effective for further study of the law of pressure relief blasting and rock-burst prediction. An explicit stress analytic function is also employed to analyze the stress distribution of the tunnel surrounding rock after pressure relief blasting based on the propagation of the blasting stress wave. Furthermore, the stress attenuation coefficient and the variation of the geo-stress distribution of surrounding rock under the pressure relief blasting were discussed in detail with the in-situ blasting test data. Highlights. The main contribution of this paper is focused on: 1. This study measures the in-situ geo-stress of surrounding rock in the ‘Sang zhu ling’ tunnel, both before and after the pressure relief blasting. This data is crucial for ensuring the safety resilience of urban infrastructure. 2. A two-dimensional explicit equation of stress attenuation was derived based on the characteristics of stress propagation, providing a more accurate analytical tool for engineers and researchers, 3. The traditionally obtained stress attenuation coefficient is shown to require modification through further in-situ experiments. Future studies will refine this coefficient, contributing to the development of more reliable methods for safeguarding urban lifelines. These contributions align with the journal’s focus on advancing urban science and engineering by promoting innovative, data-driven solutions for urban safety. 

Savar, a newly developed suburb of Dhaka, is rapidly urbanizing due to various socioeconomic and environmental factors. This study was conducted to evaluate temporal and spatial changes in Land Use and Land Cover (LULC) for the years 1980, 2000, and 2020 and predict future LULC changes. Supervised classification algorithms and cellular automata model based on Artificial Neural Networks (ANN) were used to prepare LULC maps and future simulations. The methodology was designed to overcome limitations in traditional land use and land cover change modeling, including low accuracy, computational inefficiency, and limited adaptability to complex spatial patterns. The study revealed that the rate of built-up area increased significantly over 40 years while barren land and agricultural land decreased drastically. Future LULC simulation results illustrated that the built-up area would increase by 95.07 km² (33.29%) in 2040. The model's prediction of the growth of built-up areas by 2040 demonstrated a significant rise in urban coverage with an accuracy rate of 41.14%. Therefore, this study will help us to understand the present and future urban land dynamics along with the trend of temporal and spatial LULC changes that assist planners, policymakers, and stakeholders in sustainable urban planning techniques and urban land management.

Indoor positioning technology is crucial for pedestrian navigation in large-scale scenes and the development of smart cities. Since Global Navigation Satellite Systems signal is usually blocked, complementary infrastructures are usually adopted to provide indoor positioning service such as pseudolites and Ultra-Wideband. However, such approaches still need additional user-side devices. Aiming at the problem, this paper presents a scene-matching based indoor positioning method for human indoor navigation, which utilizes smartphones as positioning carriers to achieve rapid and convenient positioning functionalities large-scale indoor scenes. Firstly, a prior database via the Oriented FAST and Rotated BRIEF feature extraction algorithm, employing a bag-of-words model for efficient feature matching, is proposed. Then, a Hidden Markov Model-based relocalization method uses historical information to quickly identify the actual corresponding place in similar indoor scenes, and the Perspective-n-Points are used to determinate the camera pose. Finally, experiments are designed and conducted to evaluate the performance of the approach. Test results indicate that our algorithm achieves an RMSE of 0.48 m, demonstrating improved stability and robustness in handling extreme cases.

Blockchain technology is believed to address trust and efficiency issues in the carbon trading market. This study aims to identify the factors that impact the adoption of blockchain technology in the carbon trading within the construction industry. Using the Technology-Organization-Environment (TOE) framework, this study explores the factors impacting the adoption of blockchain, proposing 18 influencing factors. Based on a survey involving 29 experts, an analysis of the interrelationships among factors was performed using the Decision Testing and Evaluation Laboratory (DEMATEL). The identification of key factors was then accomplished by analyzing the weight results of the DEMATEL-based Analytical Network Process (DANP). The results show that carbon trading companies contend with more environmental and technological influences, with the former dominating. Key determinants include network effect, top management support and government support. Furthermore, technological maturity additionally influences the decision-making process regarding the adoption of blockchain to a certain extent. This research provides insights influencing blockchain adoption for carbon emission trading within the construction sector, informing sustainable practices in emissions management.

Metal materials play a significant role in civil and construction engineering, and additively manufactured (AM) metals are gaining attention as a complementary technology due to their benefits of reduced material waste and greater design flexibility. In recent years, AM carbon steel, stainless steel, and alloy steel have drawn considerable interest from both academia and industry; however, their material properties are not yet fully understood. This paper compiles the mechanical properties of four materials—Ti6Al4V, 17-4PH, 304 stainless steel, and 316 stainless steel under both monotonic tensile loading and low-cycle fatigue conditions from published literatures. It examines the effects of anisotropy, surface treatment, heat treatment, and other post-processing methods on the mechanical properties of AM metals. Finally, this paper evaluates and compares the constitutive models for AM metals subjected to both monotonic tensile and low-cycle fatigue loading, providing insights to aid in the design and analysis of AM metal structures.

This paper proposes an optimal design method for the adaptive cruise control model to enhance the string stability with the adaptive cruise control (ACC). First, the influence of control gain parameters on ACC and cooperative adaptive cruise control (CACC) systems is analyzed from theoretical and numerical perspectives. Second, we compared the ACC and CACC models. On this basis, an optimal control gain parameter is proposed to consider the string stability of the ACC platoon system. Finally, we designed numerical simulation experiments to verify the effectiveness of the proposed ACC (PACC) model. Results show that compared with the classical ACC model, the PACC model has certain advantages in recovery time, vehicle average velocity, velocity standard deviation, and vehicle collision safety. Moreover, PACC is suitable for most equilibrium velocity scenarios, and it has good string stability with different time gaps, unlike the ACC and CACC models. As a result, the PACC model has better string stability and robustness. Therefore, the PACC model can enhance the string stability and provide theoretical support for designing better ACC systems.

Accurate selection of spatial-temporal features is key to the short-term traffic flow prediction model outputting higher quality results, which can effectively reduce the difficulty of constructing the prediction model. The spatial-temporal feature selection of most existing short-term traffic flow prediction models mainly relies on empirical knowledge methods and lacks interpretability. The proposed short-term traffic flow prediction network, named STFP-FGFS, utilizes a filter-genetic feature selection method to better explain the results of short-term traffic flow predictions. It consists of three stages: initial generation of temporal-spatial feature set, filtering, and feature optimization, as well as the predicted model. The initial spatial features are generated based on effective travel time, target time granularity, and vehicle type; that is, original spatial features are replaced by standardized spatial features. Four widely used feature selection methods for short-term traffic flow prediction are applied and compared, evaluating three experimental targets and four types of time granularity using four evaluation indexes. The results show that the STFP-FGFS proposed method has overall superior performance, good interpretability, and readability for selected spatial-temporal features. 

Currently, welding quality detection remains dependent on manual operation, while the increase in the span and intricacy of steel bridges has rendered the conventional method of detection insufficient to fulfill the engineering requirements. This paper presents a systematic study of welding quality detection of steel bridges based on fusion of point clouds and images in complex construction environments. (1) A welding detection system is developed that could filter out stray light and capture weld images. (2) This paper enhances the centerline extraction method in 3D reconstruction, which could effectively filter out noise interference and precisely reconstruct weld contours. The contour dimensions of both filler and cover welds are identified through feature point extraction, with an estimated detection error under 0.6%. (3) This paper optimizes the feature extraction of the Faster R-CNN network based on the appearance feature and detection need of welding defects, resulting in an improvement of 28.3 in mAP. Experimental results demonstrate that the proposed welding quality detection is both efficient and accurate, and is capable of meeting the requirements of actual steel bridge construction.

Ill-managed and unregulated urban sprawl has posed critical environmental and social challenges in the developing nations. There is an inherent need to monitor and measure the LULC changes to balance socio-economic development pressures and conservation measures. Integrating remote sensing and GIS has markedly helped frame intervention strategies in the fragile Himalayan regions. The research proposes establishing intervention strategies by monitoring LULC transitions occurring in Dharamshala, India, from 2016 to 2022. Maximum Likelihood Classification was performed on three Landsat 8 OLI images for 2016, 2019, and 2022 to prepare LULC thematic maps. The geographical and topographical complexities of the region necessitated the use of spectral vegetation indices, ancillary data, and the Strahler order algorithm to accurately represent LULC classes in the form of post-classification correction measures. The overall accuracy was found to be 88.06%, 87.02%, and 91.09% for the years 2016, 2019, and 2022. The study revealed a 140% increase in built-up areas from 2016 to 2022. The findings indicate increased developmental pressures in the 1500 m elevation and the progression of the urban sprawl towards higher altitudes, thereby increasing the risk of environmental degradation and posing a significant danger to the ecological susceptibility of the region. The tourism sector was a key factor driving LULC transitions in the area.

This study addresses the significant issue of wavefield mixing in seismic data acquired from tunnel drilling jumbo during urban tunnel construction. This paper compares and analyzes the effect of time-domain cross-correlation in the wavelet transform domain through simulation, and concludes that the combination of the time–frequency cross-correlation in the wavelet transform domain, which excels in noise suppression and in the ability of extracting related information, and the algorithm of spike deconvolution. An optimized recovery method for the wavefield characteristics of drilling jumbo drilling source is implemented. This paper focuses on the single-arm drilling source signal of drilling jumbo as the research object, and the equivalent pulse signal of the drilling source is obtained through the spike deconvolution. The equivalent pulse signal is then optimized using time–frequency cross-correlation in the wavelet transform domain, which improves the ability of extracting the valid reflective information and enhances the effect of the recovery of seismic data of drilling source of the drilling jumbo. The recovery effectiveness of the method of spike deconvolution-wavelet domain cross-correlation on the seismic recordings of the drilling source is analyzed, and the method is applied to practical engineering scenarios, thereby validating the effectiveness and feasibility of the method.

Smart highways, as vital components of intelligent transportation systems (ITS), have driven a growing demand for sophisticated sensing technologies. Among these, 4D millimeter-wave (mmWave) radar has emerged as a cornerstone, thanks to its capacity for high-resolution, real-time monitoring of complex traffic scenes. Unlike traditional radar systems, 4D mmWave not only measures the distance, velocity, and azimuth of moving objects but also captures elevation information, yielding an enriched three-dimensional understanding of the road environment. Although substantial research has been conducted on 4D mmWave radar in onboard applications, a comprehensive review focusing on its potential in roadside scenarios, such as smart highways, is currently lacking. This paper addresses that gap by first reviewing the operating principles of 4D mmWave radar and then examining how it integrates with complementary sensors. We assess its performance in key roadside functions, including multi-target detection, vehicle trajectory tracking, and macroscopic traffic flow monitoring. Further, we discuss critical challenges—such as real-time data processing bottlenecks, system scalability across extended highway networks, and suppression of inter-radar interference. By synthesizing current research and pinpointing outstanding issues, this review offers a comprehensive overview of 4D mmWave radar’s expanding role in smart highway ITS and outlines promising directions for future investigation.