Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions

Topic Information

Dear Colleagues,

In today's world, the theory and technology of civil and environmental engineering in typical regions have reached a mature stage. However, there remain numerous challenging issues related to the design, construction, and sustainable maintenance of infrastructures in regions with unique geological conditions and harsh climates. For instance, in Xinjiang Province, China, the sustainable development and operation of infrastructures face difficulties due to various inherent adverse local attributes, including frequent natural hazards such as avalanches, debris flows, earthquakes, saline and alkaline land, extreme cold and drought, significant temperature variations, high seismic intensity, and severe sandstorms. These adverse environmental and climatic conditions not only escalate construction and maintenance costs, but also result in substantial casualties and economic losses. On the other hand, these regions often boast abundant natural resources such as sunlight and wind, fostering the growth of new energy industries. However, these new energy facilities are typically located in desert areas where significant day and night fluctuations in temperature, shifting sands, and intense sandstorms occur alongside severe water scarcity. The construction of infrastructure in desert areas and the utilization of desert sand as a primary construction material represent crucial novel avenues for enhancing sustainability in civil and environmental engineering; however, these areas have been relatively unexplored. Furthermore, the challenging environment limits the use of conventional sensors. Hence, it is urgent that new sensors capable of withstanding adverse environmental conditions are designed and developed, while ensuring consistent performance. Additionally, remote sensing technologies such as InSAR will prove invaluable in monitoring terrestrial changes and infrastructure displacement in remote and inaccessible areas. However, existing methods, frameworks, tools, and platforms for processing satellite data, visualizing information, and integrating data from ground-based monitoring systems, which are well-established in conventional regions, must be modified or newly created to suit the demanding geological and climatic conditions of these areas.

The aim of this Topic is to highlight recent advancements in fundamental theories, analytical methodologies, intelligent frameworks, advanced numerical modeling, and innovative practical technologies in civil and environmental engineering and related disciplines such as architecture and surveying for regions with challenging geological and climatic conditions. We welcome submissions that address various topics including architectural design theory, remote sensing methodologies, green buildings, sustainable infrastructure management, construction technologies, novel structural patterns, waste material recycling and reuse, water treatment, pavement engineering, avalanche disaster prevention and mitigation, and disaster prevention early warning systems. We particularly encourage the submission of practical case studies that showcase the application of innovative technologies in the aforementioned fields and that contribute to enhancing sustainability and economics. Submissions should be original and not previously reported or published in other journals.

Dr. Xin Huang
Dr. Qinghua Lei
Dr. Tao Zhao
Prof. Dr. Liangfu Xie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Technologies technologies	4.2	6.7	2013	21.1 Days	CHF 1600	Submit
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (8 papers)

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25 pages, 7465 KiB  

Open AccessArticle

Influence of Horizontal Distance Between Earthmoving Vehicle Load and Deep Excavation on Support Structure Response

by Ping Zhao, Zhanqi Wang, Youqiang Qiu and Panpan Guo

Buildings 2024, 14(11), 3604; https://doi.org/10.3390/buildings14113604 - 13 Nov 2024

Viewed by 573

Abstract

The objective of this paper is to investigate the influence of earthmoving vehicle load position on the deformation and internal force characteristics of a deep excavation (DE) support structure. The position of the earthmoving vehicle load near a DE is described by the [...] Read more.

The objective of this paper is to investigate the influence of earthmoving vehicle load position on the deformation and internal force characteristics of a deep excavation (DE) support structure. The position of the earthmoving vehicle load near a DE is described by the horizontal distance between the earthmoving vehicle load and the DE. A two-dimensional finite element model is established for simulating DE engineering under the earthmoving vehicle load. The load of the earthmoving vehicle is treated as the static load, and the influence of the earthmoving vehicle load on the excavation support structure is considered from the static point of view. The numerical results of the finite element model agree well with the measured data from the field, which verifies the validity of the model. On the basis of this model, multiple models are established by changing the horizontal distance (D) between the earthmoving vehicle and the DE. The influence of D on the support structure and its critical magnitude for ensuring safety were studied. The results show that the underground diaphragm wall (UDW) is the main component for which horizontal displacement occurs under the earthmoving vehicle load. The horizontal displacements of the support structure exhibit an asymmetric distribution. When D decreases from 20 m to 0.5 m, the horizontal displacement of the UDW near the loading side increases, and the maximum horizontal displacement occurs at the top of the excavation support structure. The critical magnitude of D for ensuring safety is found to be 1 m. When D is less than 1 m, the DE is in an unsafe state. The UDW is the main component subject to the bending component. The bending moment distribution exhibits an “S” shape. The maximum bending moment increases with the decrease in D, and it occurs at the intersection of the second support and the UDW. As D decreases, the axial force in the first internal support changes from pressure to tension. The axial forces in the second and third internal supports are both pressures. The axial force in the third internal support is the largest. The research results have a positive effect on the design and optimization of DE support structures under the earthmoving vehicle load. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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20 pages, 6895 KiB  

Open AccessArticle

Three-Dimensional Reconstruction of Retaining Structure Defects from Crosshole Ground Penetrating Radar Data Using a Generative Adversarial Network

by Donghao Zhang, Zhengzheng Wang, Yu Tang, Shengshan Pan and Tianming Pan

Remote Sens. 2024, 16(21), 3995; https://doi.org/10.3390/rs16213995 - 28 Oct 2024

Viewed by 789

Abstract

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction [...] Read more.

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator’s performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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38 pages, 2270 KiB  

Open AccessArticle

The Role of Machine Learning in Enhancing Particulate Matter Estimation: A Systematic Literature Review

by Amjad Alkhodaidi, Afraa Attiah, Alaa Mhawish and Abeer Hakeem

Technologies 2024, 12(10), 198; https://doi.org/10.3390/technologies12100198 - 15 Oct 2024

Viewed by 2089

Abstract

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM_2.5 and PM₁₀. This review paper presents a comprehensive systematic assessment of machine learning (ML) [...] Read more.

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM_2.5 and PM₁₀. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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24 pages, 4109 KiB  

Open AccessReview

A Review of Acoustic Emission Source Localization Techniques in Different Dimensions

by Alipujiang Jierula, Cong Wu, Abudusaimaiti Kali and Zhixuan Fu

Appl. Sci. 2024, 14(19), 8684; https://doi.org/10.3390/app14198684 - 26 Sep 2024

Viewed by 1648

Abstract

Acoustic emission (AE) source localization technology, since the early application to one-dimensional structures, has been extended to a wide range of applications to two-dimensional (2D) structures, including isotropic and anisotropic materials, which are currently the most widely studied and the most mature. With [...] Read more.

Acoustic emission (AE) source localization technology, since the early application to one-dimensional structures, has been extended to a wide range of applications to two-dimensional (2D) structures, including isotropic and anisotropic materials, which are currently the most widely studied and the most mature. With the development of AE source localization technology, more and more significant challenges have arisen for three-dimensional (3D) structures, which are mostly anisotropic and have complex propagation paths. This paper summarizes and discusses the AE source localization methods in different dimensions as well as their applications, including the main methods for 2D AE source localization, such as the triangulation method, beam forming, strain rosette technique, modal AE, artificial neural network, optimization and the time reversal technique, as well as state-of-the-art AE source localization methods in isotropic and anisotropic structures utilizing these methods. Recent advances in AE source localization in complex 3D structures are also reviewed. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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22 pages, 16935 KiB  

Open AccessReview

Evolving Trends in Smart Building Research: A Scientometric Analysis

by Xuekelaiti Haiyirete, Wenjuan Zhang and Yu Gao

Buildings 2024, 14(9), 3023; https://doi.org/10.3390/buildings14093023 - 23 Sep 2024

Viewed by 2244

Abstract

Background: Smart building, as an emerging building concept, has been a key driving force for the transformation and upgrading of the building industry; Methods: To better understand the latest research progress and trends in the field of smart building, this study uses CiteSpace [...] Read more.

Background: Smart building, as an emerging building concept, has been a key driving force for the transformation and upgrading of the building industry; Methods: To better understand the latest research progress and trends in the field of smart building, this study uses CiteSpace 6.2.R4 bibliometric software to visualize, analyze, and interpret the literature related to the field of “Smart Building” in the WoS database from 2014 to 2023; Results: As a cross-sectoral and multidisciplinary field, smart building has received significant attention in recent years, with a rapid growth in the number of publications. International cooperation is strong, with China, the United States, and South Korea leading in the number of publications, but there is still room for enhanced collaboration among institutions. Keyword analysis shows that technology and humanized design are both crucial, and emerging technology has become the current research hotspot. Conclusions: The field of smart building has gained global attention, and more breakthroughs will be made in improving building efficiency, reducing energy consumption, and enhancing the user experience. This development is moving towards a smarter and more sustainable direction that will bring greater benefits to human life and the environment. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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17 pages, 3706 KiB  

Open AccessArticle

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

by Xuan Tang, Chong Shi and Yuming Zhang

Appl. Sci. 2024, 14(13), 5822; https://doi.org/10.3390/app14135822 - 3 Jul 2024

Cited by 1 | Viewed by 962

Abstract

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new [...] Read more.

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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15 pages, 4557 KiB  

Open AccessArticle

Experimental Study on Key Techniques for the Construction of High Asphalt Concrete Core Rockfill Dam under Unfavorable Geological Conditions

by Hao Li, Jianxin He, Shihua Zhong, Liang Liu and Wu Yang

Buildings 2024, 14(7), 1968; https://doi.org/10.3390/buildings14071968 - 28 Jun 2024

Viewed by 950

Abstract

Asphalt concrete core dams (ACCDs) have been widely constructed in Xinjiang, yet the design of materials and structures has mainly relied on empirical knowledge without substantial theoretical grounding. In this study, we carried out a large-scale relative density test of gravel material in [...] Read more.

Asphalt concrete core dams (ACCDs) have been widely constructed in Xinjiang, yet the design of materials and structures has mainly relied on empirical knowledge without substantial theoretical grounding. In this study, we carried out a large-scale relative density test of gravel material in Bamudun dam, studied the compaction characteristics of gravel material, and determined the relative density characteristic index, in order to provide a basis for the subsequent dam material rolling test and construction quality inspection. Furthermore, in order to improve the efficiency of dam construction in narrow valleys, we optimized the connection type between asphalt concrete core wall and bedrock, and proposed a rapid construction method of paving core wall after pouring mass concrete base on bedrock. Finally, we established a three-dimensional finite element model to systematically analyze the stress and deformation patterns of the dam body, core wall, and base of the ACCD at Bamudun. The results show that the maximum compressive stress suffered by the core wall during the full storage period is 1.62 MPa, there is no tensile stress, and the risk of hydraulic splitting is small. The stress and deformation levels of each part are within the safe range. This verifies the rationality of the rapid construction method. The research findings can provide a great theoretical significance and engineering value for the safe design and construction of ACCDs. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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19 pages, 7713 KiB  

Open AccessArticle

The Fractal Characteristics of Ground Subsidence Caused by Subway Excavation

by Yongjun Qin, Peng He, Jiaqi Zhang and Liangfu Xie

Appl. Sci. 2024, 14(12), 5327; https://doi.org/10.3390/app14125327 - 20 Jun 2024

Viewed by 808

Abstract

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal [...] Read more.

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details. Full article

(This article belongs to the Topic Recent Advances in Sustainability Practice of Civil and Environmental Engineering in Regions with Challenging Geological and Climatic Conditions)

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