Abstract The rapid advancement of artificial intelligence (AI) has reshaped the employment market, triggering widespread anxiety among college students about their future careers and posing a potential threat to their career decisions. Grounded in Career Construction Theory, this study investigated the impact mechanism of AI anxiety on career decisions among 315 Chinese college students, utilising a questionnaire survey and structural equation modeling (SEM). The analysis specifically examined the mediating role of career adaptability and the moderating role of self-efficacy. The results indicated that AI anxiety not only directly and negatively predicted career decisions but also exerted an adverse indirect effect by undermining career adaptability, with this mediating effect accounting for 63.35% of the total effect. However, the moderating effect of self-efficacy was insignificant, indicating limited buffering capacity. These findings suggest that higher education institutions should promote outcome-based education (OBE) reforms, enhance students’ career adaptability by universalising AI literacy and career planning courses, and deepen industry-education integration. Such measures can help students make more confident and clear-sighted career decisions in the AI era.
Opeoluwa Akinradewo, Mohamed Hafez, John Aliu
et al.
The construction industry has been tasked to adapt to technological advancements that other industries have implemented to grow and remain relevant. One of these technological advancements is augmented reality technologies. ART combines real and virtual worlds without completely immersing the individual in a virtual simulation. The use of ART can significantly improve education and training, especially in the construction industry, by analysing real-world environments while training in a controlled setting. This study, therefore, sets out to identify the factors that hinder the use of ART in the built environment. To achieve this, a quantitative research approach was adopted, and questionnaires were distributed to professionals in the built environment using South Africa as the research location. Retrieved data were analysed using both descriptive and inferential statistics. Findings revealed that investment cost is the major hindrance stakeholders face in implementing ART for education and training in the built environment. The exploratory factor analysis result clustered the identified barriers as internal organisation-related, culture-related, knowledge-related, and educator-related barriers. The study concluded that stakeholders in the built environment still have major responsibilities to ensure there is proper awareness of the benefits of adopting ART for education and training.
The Industry Foundation Class (IFC)-based sensor monitoring information expression mechanism is discussed, and an IFC-based tunnel entity definition and sensor monitoring information expansion method are proposed. Based on the existing IFC standards, by introducing the description dimensions of the tunnel’s spatial and geometric structure, the definition of IFC tunnel entities is creatively supplemented. For the first time, the expansion of IFCs in the field of tunnels is achieved, significantly expanding the boundaries of IFCs in complex underground engineering applications. The IFC-based tunnel monitoring information model is constructed using IfcSensor as the sensor entity and extending the sensor entity attribute set. Aiming at the problems of complicated tunnel monitoring data and difficult storage, this paper studies the tunnel monitoring information integration and visual early warning method based on IFCs. A Building Information Modeling (BIM)-based monitoring information integration system is developed, and the engineering application is carried out with the Jianyuan–Kaiyuan Road tunnel project in Xi‘an as a demonstration case. The advantages of BIM technology in a model visualization application are verified, and the risk perception and visual warning of tunnel construction are realized.
Mohammad Ali Nasle Seraji, Mehdi Aliehyaei, Amirhooman Hemmasi
et al.
The building sector consumes a significant amount of energy consumption among different energy-consuming sectors (about 29%). Optimizing and reducing energy consumption is necessary and inevitable. This study examines the feasibility of using 3D technology instead of traditional building construction methods. 3 scenarios are compared for the building with 3D envelope. The result shows that the effectiveness use of PCM is completely dependent to the climate of the case study. The LCA of these three scenarios are also examined. The obtained environmental results show that 3D technology requires less energy and time to construct the building. It also reduces energy consumption during the operation phase (scenarios 1 and 2). From the endpoint environmental impact view, 3D cement pollutants are more desirable than Portland cement and the impacts show less pollution for the environment. Finally, it was concluded that by using the 3D method carbon dioxide emissions reduce by 3 times.
Industrialization and increased energy use are leading to a greater influence of environmental and climate challenges on human existence and progress. China’s emissions in 2023 totaled 12.6 gigatons, representing 35% of global emissions, establishing it as the top carbon emitter globally. Combined with China’s industrial structure, it is essential to investigate carbon reduction in the building sector due to its significant contribution to carbon emissions. This study introduces a third-party organization into the relationship between stakeholders, based on traditional government regulation. It constructs a three-party dynamic evolution model involving the government, environmental protection organization, and construction enterprise. The study analyzes the evolution process of the three-party strategy selection using evolutionary game theory. We analyze the elements influencing decision-making for the three parties through simulation analysis and provide appropriate recommendations. The study’s findings indicate that low-carbon construction in China’s construction sector is an intricate system involving several stakeholders, each guided by their own interests when determining their behavioral methods. Government penalties and financial subsidies can influence construction enterprises to adopt low-carbon production practices to some degree, but excessive rewards and punishments may not support system stability.
Assel Kozhakhmetova, Almas Mamyrbayev, Aknur Zhidebekkyzy
et al.
The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.
Acknowledgments This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).
In this study, the feasibility of using ceramic wastes in the production of blended cement was evaluated by substituting limestone with ceramic waste at the percentages of 5, 10, 15, 20, and 28% before the milling stage. The chemical, physical, and mechanical properties of the cements were determined according to relevant standards, and the results were compared. The chemical analysis showed that the SiO<sub>2</sub> content of the cements increased with higher ceramic waste substitution percentages, while the CaCO<sub>3</sub> content decreased. The grindability of cements decreased with increased ceramic waste ratios, slightly reducing the Blaine specific surface area values. The water consistency for the cements was set at 28%, and all the cements met the standard limitations. The spread diameters for all types of cements were similar and practically usable in terms of workability. The cements containing ceramic waste either maintained or extended the setting time. All cements with ceramic waste exhibited higher flexural and compressive strength compared to the reference cement. The highest flexural strengths were achieved with a 28% ceramic waste substitution ratio across all curing ages. Regarding compressive strengths, all cements exhibited higher compressive strength than 10 MPa at 2 days and 32.5 MPa at 28 days, classifying them as 32.5 R-type blended cements. When the medium- (56–90 days) and long-term (365 days) compressive strengths were compared, the highest strength values were obtained from the cement with a 28% ceramic waste substitution. Although limestone-blended cement is emerging as a promising alternative to traditional Portland cement, these types of cement still contribute to environmental degradation due to the extraction of natural limestone resources through quarrying. This study showed that blended cements can be produced using ceramic waste, providing a more sustainable and environmentally friendly solution for the construction industry.
LIU Yaobin, DENG Weifeng, LI Shuoshuo, WEI Guoen, LI Ruzi
[Objective] Whether “digitization” and “green” can cooperate is one of the important scientific issues in the field of high-quality development and high-level protection research. The Yangtze River Economic Belt, as a significant cluster of the digital industry and a key area for pollution reduction and carbon emission reduction coordination, plays a pivotal role in the construction of a beautiful China and the realization of the dual carbon goals. [Methods] Based on the equity investment data of the digital industry and the environmental pollution and carbon emission data from 108 cities in the Yangtze River Economic Belt between 2010 and 2020, this study delineated the spatial pattern of the digital industry and the spatial and temporal change characteristics of pollution reduction and carbon emission reduction. A fixed-effect model was employed to explore the impact of the digital industry agglomeration on pollution reduction and carbon emission reduction coordination and mechanism. Additionally, a spatial Durbin model was constructed to analyze its spatial spillover effects and attenuation boundaries. [Results] The findings reveal that: (1) The investment network of the digital industry in the Yangtze River Economic Belt exhibits a “multi-pathway, multi-polar” trend of spatial change, with net investment flows showing a clear tendency towards suburbanization, forming a “outflow from the center, receiving by the periphery” distribution pattern. (2) The agglomeration of the digital industry positively drives pollution reduction and carbon emission reduction, also demonstrating synergistic effects for both. This result remains robust after a series of endogeneity and robustness tests. Mechanism tests found that digital industry agglomeration can promote regional pollution reduction and carbon emission reduction coordination through scale economy effects, industrial upgrading effects, and technological innovation effects. (3) Digital industry agglomeration has a significant positive spatial spillover effect on pollution reduction and carbon emission reduction coordination. This effect presents a “right-tailed U-shaped” characteristic with increasing spatial distance. Meanwhile, the area within 250 km of the agglomeration is the “enhancement area” for spatial spillover effects, the area between 250 km and 450 km is the “attenuation area”, and beyond 450 km, the effect approaches zero and becomes insignificant. [Conclusion] Research has confirmed that leveraging the agglomeration effects of the digital industry can effectively promote synergistic improvements in regional pollution reduction and carbon emission mitigation.It is recommended to foster the agglomeration and networking development of the digital industry, encourage cross-regional investment in the digital industry, and implement differentiated strategies and cross-regional cooperation platforms.
Oily fine particles are an important air pollutant in industrial environments. Workers exposed to oil mist for a long time face great health risks. Particle growth pretreatment is a technical principle to increase particle size and improve purification efficiency. Acoustic waves are commonly used to acheive particle growth, and a large number of acoustic wave agglomeration experiments have been carried out on non-oil fog. However, studies on oily particles are few. On the basis of previous studies on acoustic agglomeration of non-oily particles, this experiment designed a set of experimental equipment to compare the agglomeration effect of oily and non-oily particles. It was found that the agglomeration effect ratio of oily and non-oily particles to φ1oiliness/φ1non-oily particles was greater than 1. Therefore, the agglomeration effect of oily particles under stationary acoustic waves was more obvious. Results clearly show that oily particles have a higher agglomeration ability. In this study, a traditional ventilation and purification technology was expanded to include sound agglomeration technology into the pretreatment stage of purification and dust removal, thereby demonstrating feasibility of improved purification efficiency of an oily fine particle purification system, and laying a foundation for engineering applications.
Nikolai S. Zakharov, Nikolai O. Sapozhenkov, Vladimir A. Rakitin
et al.
Russia’s dominance in the global energy market is predetermined by the scale and development prospects of gas transmission infrastructure to ensure a continuous cycle of gas supply from fields to end consumers. Operating conditions of gas production, processing, transportation, storage and distribution facilities are associated with the influence of environmental factors, which varies greatly due to the length of pipelines covering all climatic regions of the country. The growth in the length of new pipelines and the need to repair existing pipelines based on the results of in-line diagnostics contribute to an increase in the volume of loading and unloading operations and the storage of pipes, therefore, studies on improving methods for determining the need for truck cranes when for constructions organizing technological processes and repair of main gas pipelines are relevant.
The purpose of the study is to determine the efficiency of truck cranes operation in the process of main gas pipelines overhaul and construction.
The scientific novelty is formed by the patterns of formation of the cost and labor intensity of loading, depending on the load capacity of a truck crane and the unloading pipes parameters.
The research methodology is based on a systematic approach, methods for analyzing the technical operation of vehicles and proven data processing techniques.
The recommendations made as a result of this study have been implemented to organize preparatory work at major main gas pipelines overhaul facilities and construction, which increases the efficiency of processes, optimizes the structure of special vehicles and reduces the cost of technological operations.
Elydio Soares, Talita santos, Filipe Mazzaro
et al.
Brazil is the world's largest supplier of niobium to industry, accounting for 98% of world production, with Minas Gerais supplying 80% of total production. The mineral exploration industry generates millions of tons of waste annually. In several mining industries, waste is considered a burden for companies. Based on the radiation protection exemptions for the disposal of mining waste, the study analyses the use of waste as a raw material for the construction industry. The minimum dose rate found for gamma radiation in the waste was 0.24 µSv/h and a maximum dose of 0.33 µSv/h, which corresponds to an annual dose above the population exposure limit. The radio concentrations from gamma spectrometric analyses with the Ge(HP) detector for the two samples are a maximum of 240 Bq/kg for Ra-226 and a maximum of 840 Bq/kg for Ra-228. Despite the dose values determined for gamma radiation, CNEN Resolution 179 of 2014 considers materials with natural radioactive concentrations of radium 226 and 228 of up to 1000 Bq/kg suitable for use in the cement industry. Nevertheless, further analysis must be carried out. Since the tailings contain a concentration of Ra-226 and the radio is a source of radon gas, new analyses need to be carried out targeting the exhalation of radon.
Medical physics. Medical radiology. Nuclear medicine, Radioactivity and radioactive substances
Used cooking oil after the thermal processing of food constitutes a difficult-to-degrade waste product, the quantities of which are increasing yearly due to the increasing pace of life and the establishment of new food service outlets. Frying allows for the preparation of a large amount of food for consumption in a short time but alters the physical and chemical properties of the oil used, which then becomes harmful to human health. Despite several possibilities for using waste cooking oil, environmentally safe ways to manage it are still being sought. In an effort to reduce the amount of waste, using cooking oil as a binder for the benefit of the construction industry seems plausible. This paper presents a literature review on the use of waste cooking oil to produce composite materials for construction purposes, addressing the process parameters of tipping solid materials comprising vegetable oil as a binder and examining their strength and absorbability. Methods of obtaining oil binders, either comprising vegetable oil alone or various mixtures, are described. In addition, the advantages of producing and using “green” materials are presented.
Steel is widely used as reinforcement for brittle structural materials such as concrete structure and unreinforced masonry structure (URM). However, the job wasted in steel reinforcement installation and the following corrosion hinder the development of construction industry. The emergence of strain-hardening cement composites (SHCC) provides an opportunity for steel-free construction. This paper provides a comprehensive review of the properties of SHCC and the corresponding practical exploration without reinforcement. The authors herein begin with a discussion on the superior properties of SHCC and its structural applications on the RC structure. Following this, the application of SHCC to retrofit URM is reviewed. Finally, we presents the advances of SHCC used in 3D concrete printing (3DCP) technology, and discuss the feasibility of SHCC structures without reinforcements in the future. When these explorations are coupled with appropriate theoretical models, true values for auto-construction without steel reinforcement will emerge.
Ghassan Almasabha, Odey Alshboul, Ali Shehadeh
et al.
The rapid growth of using the short links in steel buildings due to their high shear strength and rotational capacity attracts the attention of structural engineers to investigate the performance of short links. However, insignificant attention has been oriented to efficiently developing a comprehensive model to forecast the shear strength of short links, which is expected to enhance the steel structures’ constructability. As machine learning algorithms was successfully used in various fields of structural engineering, the current study fills the gap in estimating the shear strength of short links using sophisticated machine learning algorithms. The deriving factors such as web and flange slenderness ratios, the flange-to-web area ratio, the forces in web and flange, and the link length ratio were investigated in this study, which is imperative to formulate an integrated prediction model. Consequently, the aim of this study utilizes advanced machine learning (ML) models (i.e., Extreme Gradient Boosting (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mi>G</mi><mi>B</mi><mi>O</mi><mi>O</mi><mi>S</mi><mi>T</mi></mrow></semantics></math></inline-formula>), Light Gradient Boosting Machine (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>i</mi><mi>g</mi><mi>h</mi><mi>t</mi><mi>G</mi><mi>B</mi><mi>M</mi></mrow></semantics></math></inline-formula>), and Artificial Neural Network (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>N</mi><mi>N</mi></mrow></semantics></math></inline-formula>) to produce accurate forecasting for the shear strength. In this study, publicly available datasets were used for the training, testing, and validation. Different evaluation metrics were employed to evaluate the prediction’s performance of the used models, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R<sup>2</sup>). The prediction result displays that the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mi>G</mi><mi>B</mi><mi>O</mi><mi>O</mi><mi>S</mi><mi>T</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>i</mi><mi>g</mi><mi>h</mi><mi>t</mi><mi>G</mi><mi>B</mi><mi>M</mi></mrow></semantics></math></inline-formula> provided better, and more reliable results compared to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>N</mi><mi>N</mi></mrow></semantics></math></inline-formula> and the AISC code. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mi>G</mi><mi>B</mi><mi>O</mi><mi>O</mi><mi>S</mi><mi>T</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>i</mi><mi>g</mi><mi>h</mi><mi>t</mi><mi>G</mi><mi>B</mi><mi>M</mi></mrow></semantics></math></inline-formula> models yielded higher values of R<sup>2</sup>, lower (RMSE), (MAE), and (MAPE) values and have shown to perform more accurate. Therefore, the overall outcomes showed that the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>i</mi><mi>g</mi><mi>h</mi><mi>t</mi><mi>G</mi><mi>B</mi><mi>M</mi></mrow></semantics></math></inline-formula> outperformed the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mi>G</mi><mi>B</mi><mi>O</mi><mi>O</mi><mi>S</mi><mi>T</mi></mrow></semantics></math></inline-formula> model. Moreover, the overstrength ratio predicted by the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>i</mi><mi>g</mi><mi>h</mi><mi>t</mi><mi>G</mi><mi>B</mi><mi>M</mi></mrow></semantics></math></inline-formula> showed an excellent performance compared to the Gene Expression and Finite Element-based models. The developed models are vital for practitioners to predict the shear strength accurately, which pave the road towards wider application for automation in the steel buildings.
Yakov Ye. Lvovich, Andrey P. Preobrazhenskiy, Yuriy P. Preobrazhenskiy
The paper deals with the problem associated with the study of the characteristics of the transport system in the region. Possibilities of using an integrated approach that takes into account spatio-temporal information.
Liulchenko Yevhen, Sakhno Serhiy, Sergiienko Tetiana
et al.
In connection with the growing need for saving natural resources used in aggregates for concrete, the importance of lightweight structural aggregates obtained from production waste is continuously increasing. Lightweight structural concretes on porous aggregates can significantly reduce own weight of structures, make it possible to manufacture larger structures, reduce transport costs, and improve the thermal insulation and acoustic properties of enclosing structures. The use of waste from the mining and metallurgical industry to produce construction materials significantly reduces environmental pollution. The article is devoted to studying the possibility of using wastes from the mining and processing industry of enterprises of the Kryvyi Rih iron ore basin to produce lightweight porous aggregate. The paper presents the results of studies of the effect of the charge's granulometric composition, the quantitative content of the raw mix components, and the temperature of heat treatment on aggregate quality. The most suitable raw material mixture for artificial aggregate has been determined. The results of X-ray diffraction thermographic analysis of raw granules are presented. The influence of technological factors on the aggregate density and strength has been studied using mathematical modeling. The obtained equations made it possible to reveal the regularities of the raw mixture's components and temperature for the optimal aggregate density and strength. The results of studying the structure and porosity of the developed aggregate are presented. The results of X-ray thermographic analysis of the aggregate explain the mechanism of pore formation in the pellets. The basic physical and mechanical properties of the obtained aggregate are investigated, particularly attention pairing to the study of the aggregate’s contact zone with the cement stone.
This paper focuses on an unpublished topic, the 2022 World Cup stadiums. The somewhat unusual research subject will tend to question their role as real protagonists of this sporting event. Through this article the main purpose is to understand how Qatar thinks its new sports venues. These stadiums turn out to be central elements of its world communication by a staging of its territory, but also symbolic places in the construction of the representation of its power within the Gulf and its society. As future centers of attention, in 2022, these stadiums are also a reflection of many barriers that the emirate faces in the recent direction of its foreign policy on a global scale. Reflections of the acceleration of the political time of the emirate on the world stage, the stadiums also testify to the gap which persists between the global image that the power tries to project from Qatar and the internal realities to its society. Finally, by their centrality during the next World Cup these stadiums concentrate the struggles for leadership that are playing out on the Gulf political scene and so around this major event. Beyond these new sports venues, the goal is to put in an historical and geopolitical perspective the development of sport in Qatar to be able to understand the issues and the subtle logic of the recent integration of the emirate within the spheres of the sport show industry.
Ethnology. Social and cultural anthropology, Cities. Urban geography
The development trends of the adaptive management system for construction enterprises are determined. It is noted that the construction industry creates a huge number of jobs, consuming the products of many other sectors of the national economy. It is mentioned that the development of construction enterprises requires constant monitoring of the position of an enterprise and its competitors in the construction market, research on the strengths and weaknesses of its own financial and economic activities, and the ability to adapt to changes in both the internal and external environment. The goal, object and subject of adaptive management are determined. The most promising approaches aimed at introducing adaptive changes in a construction enterprise are highlighted. It is found that today monitoring is one of the main elements that form the system of not only operational but also strategic management. It is a functional area of the economic activity of a construction enterprise and is closely linked with the implementation of management functions necessary for making operational and strategic management decisions. Considering the above objective reasons for the formation of adaptive management in enterprises in the construction industry, an algorithm for transforming the system (a construction enterprise) under adaptive management is built. It is concluded that it is advisable that enterprises in the construction industry of Ukraine (and the economy of Ukraine as a whole) apply adaptive management, which can be defined as the process of implementing an integrated system of measures and actions to ensure the interconnection and achievement of goals of all participants in the management process in an unstable external environment.