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China's architecture, engineering, and construction (AEC) industry needs a clear and sustainable development roadmap. Drawing inspiration from speeches delivered at a seminar hosted by the Chinese Academy of Engineering and extensive literature research on Chinese policies, this article presents a summary of the current trends in the AEC industry in four dimensions: industrialization as the foundation, intelligence as the enabler, lean management as the strategy, and green development as the goal. These four dimensions are intricately interconnected and rooted in multiple disciplines. The article provides a detailed review of the current practices, challenges, and future directions associated with each dimension. Additionally, ten grand challenges were proposed to stimulate discussions on the future of the AEC industry. This article offers an overarching understanding of the AEC industry and presents a four-dimension framework for sustainable development, which can be valuable for AEC practitioners. [ABSTRACT FROM AUTHOR]

Open-pit mining activities inevitably affect the surrounding ecological environment. Therefore, it is crucial to clarify the disturbance characteristics of open-pit mining activities on the surrounding vegetation and scientifically implement ecological restoration projects. This study investigates the impact of open-pit coal mining in arid and semi-arid regions on surrounding vegetation from a vegetation phenology perspective. Initially, we construct a high-frequency time series of vegetation indices by Harmonized Landsat 8 and Sentinel-2 surface reflectance dataset (HLS). These time series are then fitted using the Double Logistic and Asymmetric Gaussian methods. Subsequently, we quantify three pivotal phenological phases: Start of Season (SOS), End of Season (EOS), and Length of Season (LOS) from the fitted time series. Finally, utilizing mine boundaries as spatial units, we create a buffer zone of 100 m increments to statistically analyze changes in phenological phases. The results reveal an exponential variation in vegetation phenological metrics with increasing distance from the mining areas of Heidaigou-Haerwusu (HDG-HEWS), Mengxiang (MX), and Xingda (XD) in northwest China. Then, we propose a method to identify the disturbance range. HDG-HEWS, MX, and XD mining areas exhibit disturbance ranges of 1485.39 m, 1571.47 m, and 671.92 m for SOS, and 816.72 m, 824.73 m, and 468.92 m for EOS, respectively. Mineral dust is one of the primary factors for the difference in the disturbance range. The HDG-HEWS mining area exhibits the most significant disruption to vegetation phenological metrics, resulting in a delay of 6.4 ± 3.4 days in SOS, an advancement of 4.3 ± 3.9 days in the EOS, and a shortening of 6.7 ± 3.5 days in the LOS. Furthermore, the overlapping disturbance zones of the two mining areas exacerbate the impact on phenological metrics, with disturbance intensities for SOS, EOS, and LOS being 1.38, 1.20, and 1.33 times those caused by a single mining area. These research results are expected to provide a reference for the formulation of dust suppression measures and ecological restoration plans for open-pit mining areas. [ABSTRACT FROM AUTHOR]

The Sichuan–Tibet Railway (STR) is currently under construction and serves as an important transportation route in western China. Identifying potential geohazards along the route is important for project construction. However, research on the frozen soil of the Western Sichuan Plateau, and on frozen soil identification using interferometric synthetic aperture radar (InSAR) is relatively negligible. As a low-cost, all-weather spatial geodesy tool, InSAR is frequently used for geohazard identification. We selected a study area located along the Litang section of the STR, starting from Litang County in the east and extending 60 km to the west. The geological conditions along the line are complex, with numerous fault zones and hidden danger points for landslide. To identify unstable slopes along the line, distribute scatterer InSAR (DS-InSAR) was used to obtain surface displacement information from 2018 to 2021. Based on the displacement information obtained from the ascending and descending orbit images from Sentinel-1, a spatial density clustering method identified 377 and 388 unstable slopes in the study area, respectively, of these, 132 were consistent. The identified unstable slopes were mostly located in areas with a relatively high altitude and moderate slope. The Luanshibao landslide, which is a typical landslide in the study area, had notable signs of displacement, where the displacement rate along the back edge of the landslide can reach 20 mm/a. An inversion method for the seasonal frozen soil area distribution was proposed based on the periodic subsidence and uplift model and time-series monitoring data; the calculated seasonal freeze–thaw amplitude exceeded 20 mm. Further analysis revealed a 2-month lag in the response of the freeze–thaw phenomenon to the air temperature. This study demonstrated that DS-InSAR offers optimal surface displacement data, which can provide an important basis to identify engineering geological hazards. [ABSTRACT FROM AUTHOR]

Engineering site and shaft pillars are excavated to prolong the life of collieries and excavate more underground coal. The Jinggezhuang colliery ('JGZ') is a resource-exhausted coal mine in eastern China. It was determined that the industrial site and shaft pillar of JGZ would be extracted in 2008. This study excavated an experimental panel to examine the effect of pillar excavation on surface buildings in complicated geological conditions. A new pillar design was proposed based on surface monitoring to increase the recovery ratio. To maintain the safety of the shaft and engineering facilities, panel 0091 was mined and surface deformation was monitored during the experiment. The deformation characteristics and parameters were obtained using a back analysis method. A new pillar was designed using the parameters measured from panel 0091. The design maintained the safety of the shaft but relaxed the restriction of the influence of constructions at the engineering site. The prediction results of the surface subsidence and the deformation of the main building were analyzed. The maximum subsidence of the surface was 7419 mm, but the surface subsidence of the shafts was less than 10 mm. The shafts were weakly influenced by the pillar excavation. The prediction results can be used as basic information for the monitoring and maintenance of buildings in the future. Using the new pillar design, 2.54 million tons of coal resources were mined. This study provides an engineering example and a reference for shaft pillar excavation in the future. [ABSTRACT FROM AUTHOR]

High-intensity and large-scale mining activities have aggravated regional eco-geological risk. Therefore, it is significantly essential to conduct an assessment of the eco-geological risk of mining areas. Although some progress has been achieved in ecological risk assessment studies, existing approaches are not entirely suitable for coal bases with high landscape fragmentation and dense coal mining activities. Here, we developed a novel landscape ecological and geological risk (LEGR) assessment framework based on theories that include landscape ecological risk and eco-geological risk. The framework selected 10 indicators, including slope, fluctuation, lithological hardness, soil type, FVC, RSEI, precipitation, biological abundance, distance to road and subsidence rate, and calculated the weights of indicators by introducing the AHP-CRITIC coupled weighting model. Then, the impact of landscape disturbances on eco-geological risk is quantified by measuring landscape losses. This framework was applied to the Shenfu mining area (SFMA), a typical coal base in northwest China. The results indicated the LEGR was moderate in the SFMA whose spatial distribution exhibited an increasing trend from southwest to northeast. Besides, the high LEGR was mainly in the aggregated mining area with high subsidence. For the eco-geological environment monitoring at the mine scale, a multiscale geographically weighted regression (MGWR) model was utilized for analyzing the relationship between indicators and LEGR within the disturbed range of coal mining. It provided valuable insights for the formulation of environmental protection policies in the mining area. • A novel landscape ecological geological risk (LEGRI) model was constructed. • The spatial characteristics of landscape ecological geological risk were analyzed. • The multiscale geographically weighted regression (MGWR) model was used to design eco-geological risk monitoring scheme at mining area scale. • The subsidence rate was calculated by SBAS-InSAR technology and used as an index of geological conditions. [ABSTRACT FROM AUTHOR]

• A linear weighted model coupling AHP and Entropy (AELW) is proposed to calculate indicator weight. • The eco-geo-environmental vulnerability (EGEV) of Zhungeer City was assessed and mapped via the AELW model and multi-source data integration. • EGEV of towns and open-pit mining aggregation areas are analyzed. • This study provides some practical suggestions for reducing regional EGEV and adaptive management. Assessing the ecological and geological environment vulnerability (EGEV) of cities arid and semi-arid regions is important for urban planning, development, and ecological protection. This study investigated the ecological and geological environmental problems and possible potential risk factors in Zhungeer City and constructed an EGEV assessment index system covering the geological environment, human activities, coal mining, and hydrological conditions. We proposed a linear weighted model to calculate the comprehensive weight of each indicator using the Analytical Hierarchy Process (AHP) and Entropy method based on distance function optimization (AELW), and frequency ratio accuracy is then introduced to compare the remarkable classification performance between AELW and other models. The frequency ratio accuracy results show that AELW model has the highest accuracy for EGEV assessment (62.77 %), followed by Multiplicative Integrated Normalization Model (MINM, 61.46 %), AHP (60.98 %), Ideal Point Model (IPM, 60.30 %) and Entropy (53.55 %). The EGEV of Zhungeer was classified as I-V from lowest into highest, among which the EGEV of I-II-III accounted for 78.15 %, EGEV of IV accounted for 16.42 %, and the EGEV of V risk area accounted for 5.43 %, with a good overall ecological and geological environment quality. These results can provide tangible management suggestions for reducing EGEV in Zhungeer city, and the AELW model provides direction for EGEV assessment in other similar areas. [ABSTRACT FROM AUTHOR]

Efficient management of construction progress requires regular progress tracking, but monitoring progress on a daily basis can be both time-consuming and labor-intensive since the meticulous manual data processing is involved. Soil-foundation construction entails multiple uncertain underground safety risks, the elimination of which requires significant time and effort, thereby increasing the likelihood of schedule overruns. Automation and augmentation of progress monitoring are necessary for soil-foundation construction. There are two primary research challenges in the context of soil-foundation construction: the precise differentiation of objects with similar apparent characteristics, and the accurate detection of partially obscured objects. The existing approaches primarily address such issues by image post-processing rather than augmenting image feature inference. To enhance the efficiency of progress monitoring and facilitate unmanned inspection, an integrated computer vision-based framework for the tracking of soil-foundation construction progress was proposed. An improved SOLOv2 was utilized to qualitatively and quantitatively recognize the construction progress. Subsequently, the actual and differential progress could be integrated into streamlined BIM based on a self-adaptive grid-based mapping method. The approach was applied to a campus building project in China, resulting in a high segmentation accuracy (mAP = 90.9%). The improved SOLOv2 was found to surpass other state-of-the-art segmentation algorithms. Further, the impact of grid size on the mapping accuracy of construction progress was explored. The present study promotes automated schedule tracking and provides a feasible approach for developing a digital twin of soil-foundation construction. • Proposed a soil-foundation construction progress automated recognition framework. • Proposed an improved instance segmentation approach based on NAS-FPN. • Provided a enhanced feature extraction algorithm leveraging Swin Transformer. • Described a self-adaptive grid-based mapping method for progress visualization. • Applied the framework to a campus building project with a mAP of 90.9%. [ABSTRACT FROM AUTHOR]

Traditional construction progress tracking relies on labor-intensive activities with time lags, potential man-made errors, and inefficient progress management, which demands for an innovative and automated progress tracking approach. This paper describes a deep learning method that utilizes image segmentation to automatically evaluate the wall construction progress of an entire floor with the progress results streamlined to BIM. The approach was applied to a case study in China for assessing plastering construction activities with high segmentation accuracy (mean average precision = 96.8%). Further improvement of Mask Region-Based Convolutional Neural Networks (Mask R-CNN) and evaluation of its superiority over other models have also been discussed. This study provides both theoretical and practical references for unmanned supervision of progress tracking and intelligent schedule management. • Described an evaluation framework to recognize wall progress of an entire floor. • Proposed an image segmentation method to calculate wall construction area. • Improved Mask R-CNN algorithm to increase segmentation accuracy. • Discussed the influence of image light on segmentation accuracy. • Applied the framework to a case study with mean Average Precision of 96.8%. [ABSTRACT FROM AUTHOR]

Objectives: Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed., Design: Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments., Results: The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM)., Conclusions: This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Accurate mapping spatiotemporal patterns of CO 2 emissions and understanding its driving factors are very important, it is useful for the scientific and rational formulation of carbon emission reduction policies. Nevertheless, due to data availability issues, most studies have been limited to the global and national scales, and the models used were relatively simple. In this paper, we used the 500 m Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data and the 250 m Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS NDVI) and proposed an improved CO 2 emissions index (ICEI) to calculate CO 2 emissions. Compared with the total nighttime light (NTL), the average regression coefficient (R 2 ) can be improve from 0.73 to 0.78. We also used the coefficient of variation, spatial autocorrelation, and geographically weighted regression models to analyze the temporal and spatial variation mode of CO 2 emissions, as well as the associated correlation and heterogeneity, at three different administrative unit scales during 2012-2019. Our experimental results demonstrate that: (1) the improved index (ICEI) is better than the traditional variable (NTL) in estimating CO 2 emissions; (2) the highest CO 2 emissions are primarily gathered in the developed coastal areas in eastern China; and (3) at the provincial level, the added value of the secondary industry is the most significant factor, whereas the added value of the tertiary industry is negatively correlated with CO 2 emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)