Hasil untuk "Construction industry"

Jorge Martinez-Gil, Mario Pichler, Nefeli Bountouni et al.

We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or collaborative, are responsible for well-defined tasks, enabling flexibility and simplifying integration. Moreover, our framework supports modular integration and maintains low resource requirements. Preliminary evaluations concerning the food industry in real scenarios indicate improved deployment time and system adaptability performance. The source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component.

Rauf Siita, Micheal Korblah Tsorgali

Although several studies have been conducted on unethical practices in the construction industry worldwide, little has been done in Ghana's Wa Metropolis, especially regarding government contracts. As a result, it is necessary to look into how unethical actions affect government construction projects in Wa Metropolis. The study used a mixed-methods approach. The survey included 212 respondents, while the interviews were conducted with 16 participants. Data were gathered using questionnaires and interviewing guides and then analysed using thematic analysis, factor analysis, Spearman's rank correlation, and descriptive statistics. The results revealed that unethical practices in the construction industry are driven by contractors' competition, bribery, profit maximisation, illegal contract awards, a lack of transparency, job insecurity, and excessive love of money. The effects of unethical practices in the construction industry were also found to include poor craftsmanship, short project lifespans, inexperienced contractors, safety concerns, fatalities, misallocation of resources, inflated project costs, and project abandonment. Overall, projects were negatively impacted by unethical behaviour in the construction industry. To enhance ethical conduct in the construction sector, the Association of Contractors should adopt and enforce a comprehensive code of ethics as a foundation for its operations, supported by robust regulatory oversight, strict adherence to public procurement laws, transparent bidding processes, and ongoing ethical awareness initiatives. The originality of this study lies in its focus on ethical issues within the construction industry, specifically in the Wa Metropolis. Unlike most prior studies in Ghana, which tend to address ethical concerns more broadly, this research uniquely concentrates on government projects.

Eman O. Yehia, Ahmed S. H. Hashad, Ahmed G. Asran

The increasing use of compressed earth blocks (CEBs) in construction, driven by their ease of manufacturing and environmental sustainability, presents a significant advantage in the building industry. However, the lower compressive strength of these blocks compared to traditional ones requires innovative approaches to enhance their load-bearing capacity. This study focuses on investigating the impact of incorporating reinforcing steel bars and grout material in CEBs walls to strengthen their structural integrity by creating Micro Columns (MC) within the walls. The research aims to determine the percentage of participation of these elements and establish the required spacing between the columns for optimal load resistance. The study reveals that MC contribute to resisting vertical loads on the wall buildings at a rate of approximately 58% of the total wall strength. The optimal distribution of MC was achieved when using a spacing distance between the columns equal to 1 m per wall length.

Liangcan Liu, Xiang Li

IntroductionDeveloping climate resilient agriculture is particularly important to reduce food security and climate risks in the context of frequent climate extremes disrupting food production systems.MethodsBased on the provincial panel data of China from 2011 to 2022, this paper uses dual machine learning model to explore the effect of digital inclusive finance (DIF) on climate resilience of food production (CRFP) and its transmission mechanism.ResultsDIF can significantly improve CRFP, and the conclusion is still valid after endogeneity and robustness tests. The mechanism of action shows that DIF can enhance CRFP by promoting agricultural technology innovation, agricultural industry agglomeration and agricultural socialized services. Heterogeneity analysis show that DIF has a significant effect on promoting CRFP in the eastern region, the main grain-producing areas and the regions with high digital infrastructure.DiscussionIt is necessary to strengthen the construction of digital infrastructure and improve the ecological compensation mechanism to give full play to the role of DIF in improving the climate resilience of grain production. This study provides evidence-based support for the realization of climate-smart agriculture, with policy implications for cracking the food crisis trap in low- and middle-income countries.

Jiao Wang, Yuchen Ma, Rui Li et al.

Sustainable risk and disaster management in the built environment has become a critical research focus amid escalating environmental challenges. Building Information Modeling (BIM) is recognized as a key digital tool for enhancing disaster resilience through simulation, data integration, and collaborative management. This study systematically reviews BIM applications in sustainable risk and disaster management from 2014 to 2024, employing the PRISMA framework, literature coding, and network analysis. Five primary research clusters are identified: (a) sustainable construction and life cycle assessment, (b) performance evaluation and implementation, (c) technology integration and digital innovation, (d) Historic Building Modeling (HBIM) and post-disaster reconstruction, and (e) project management and technology adoption. Despite increasing scholarly attention, the field remains dominated by conceptual studies, with limited empirical exploration of emerging technologies such as artificial intelligence (AI). Four key challenges are highlighted: weak foundational integration with structural risk research, technological bottlenecks in AI and digital applications, limited practical implementation, and insufficient linkage between sustainability and risk management. Future trends are expected to focus on achieving Industry 4.0 interoperability, advancing AI-driven intelligent disaster response, and adopting multi-objective optimization strategies balancing resilience, sustainability, and cost-effectiveness. This study provides a comprehensive overview of the field’s evolution and offers insights into strategic directions for future research and practical innovation.

Jiaqi Lei, Liang Hu, Yi Bu et al.

Previous researches on the Information retrieval (IR) field have focused on summarizing progress and synthesizing knowledge and techniques from individual studies and data-driven experiments, the extent of contributions and collaborations between researchers from different communities (e.g., academia and industry) in advancing IR knowledge remains unclear. To address this gap, this study explores several characteristics of information retrieval research in four areas: productivity patterns and preferred venues, the relationship between citations and downloads, changes in research topics, and changes in patterns of scientific collaboration, by analyzing 53,471 papers published between 2000 and 2018 from the Association for Computing Machinery (ACM) Digital Library dataset. Through the analysis and interpretation on empirical datasets, we find that academic research, industry research, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrieval has become richer over time in terms of themes, foci, and sub-themes, becoming a more diverse field of study.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Lei Ren, Haiteng Wang, Jinwang Li et al.

With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

Razin Farhan Hussain, Mohsen Amini Salehi

The Industry 4.0 revolution has been made possible via AI-based applications (e.g., for automation and maintenance) deployed on the serverless edge (aka fog) computing platforms at the industrial sites -- where the data is generated. Nevertheless, fulfilling the fault-intolerant and real-time constraints of Industry 4.0 applications on resource-limited fog systems in remote industrial sites (e.g., offshore oil fields) that are uncertain, disaster-prone, and have no cloud access is challenging. It is this challenge that our research aims at addressing. We consider the inelastic nature of the fog systems, software architecture of the industrial applications (micro-service-based versus monolithic), and scarcity of human experts in remote sites. To enable cloud-like elasticity, our approach is to dynamically and seamlessly (i.e., without human intervention) federate nearby fog systems. Then, we develop serverless resource allocation solutions that are cognizant of the applications' software architecture, their latency requirements, and distributed nature of the underlying infrastructure. We propose methods to seamlessly and optimally partition micro-service-based application across the federated fog. Our experimental evaluation express that not only the elasticity is overcome in a serverless manner, but also our developed application partitioning method can serve around 20% more tasks on-time than the existing methods in the literature.

H. Hinduja, N. Mandal

The COVID-19 pandemic has had a long-term impact on industries worldwide, with the hospitality and food industry facing significant challenges, leading to the permanent closure of many restaurants and the loss of jobs. In this study, we developed an innovative analytical framework using Hamiltonian Monte Carlo for predictive modeling with Bayesian regression, aiming to estimate the change point in consumer behavior towards different types of restaurants due to COVID-19. Our approach emphasizes a novel method in computational analysis, providing insights into customer behavior changes before and after the pandemic. This research contributes to understanding the effects of COVID-19 on the restaurant industry and is valuable for restaurant owners and policymakers.

Yannis Spyridis, Vasileios Argyriou, Antonios Sarigiannidis et al.

The escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion industry. This paper addresses the inefficiencies of traditional textile sorting methods by introducing an autonomous textile analysis pipeline. Utilising robotics, spectral imaging, and AI-driven classification, our system enhances the accuracy, efficiency, and scalability of textile sorting processes, contributing to a more sustainable and circular approach to waste management. The integration of a Digital Twin system further allows critical evaluation of technical and economic feasibility, providing valuable insights into the sorting system's accuracy and reliability. The proposed framework, inspired by Industry 4.0 principles, comprises five interconnected layers facilitating seamless data exchange and coordination within the system. Preliminary results highlight the potential of our holistic approach to mitigate environmental impact and foster a positive shift towards recycling in the textile industry.

Hanlong Wan, Jian Zhang, Yan Chen et al.

This paper investigates the transformative potential of Generative AI (Gen-AI) technologies, particularly large language models, within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as automated compliance checking and building design assistance. The research highlights how Gen-AI can automate labor-intensive processes, significantly improving efficiency and reducing costs in building practices. The paper first discusses the two widely applied fundamental models-Transformer and Diffusion model-and summarizes current pathways for accessing Gen-AI models and the most common techniques for customizing them. It then explores applications for text generation, such as compliance checking, control support, data mining, and building simulation input file editing. Additionally, it examines image generation, including direct generation through diffusion models and indirect generation through language model-supported template creation based on existing Computer-Aided Design or other design tools with rendering. The paper concludes with a comprehensive analysis of the current capabilities of Gen-AI in the building industry, outlining future directions for research and development, with the goal of paving the way for smarter, more effective, and responsive design, construction, and operational practices.

Norawit Sang-rit, Bhumiphat Gilitwala

Purpose – This study aims to determine the factors influencing employee retention working in construction-related small-medium enterprises (SMEs) in Krung Thep Maha Nakhon. The study contributes to the construction site manager getting insight into employees' desired goals in the workplace. Furthermore, the study provided information about the diversity of generations (age groups), income levels and educational levels of employees working in the construction industry in the Krung Thep area. Design/methodology/approach – The researcher decided to investigate a sample size of 386 respondents based on the target population. A purposive sampling method was selected by giving out questionnaires to the respondents employed in construction-related SMEs in Krung Thep. The questions comprised two major parts, which are demographic questions and measuring variables relevant to the independent variables. Findings – The study's aim of findings is to investigate the factors that retain the employees who are pursuing their careers in construction-related SMEs. The findings of this research are to unveil that task interdependence significantly contributes to agile working. Lastly, employee retention is significantly affected by agile working among employees in an organisation. Research limitations/implications – This research only studies factors influencing employee retention among those of all ranges of ages, incomes and educational levels working in construction-related SMEs. The researcher collected data on the income level, age group and educational level of employees to use for further study. Originality/value – The study is about determining the factor that affects agile working and employee retention among those working in construction-related SMEs.

Mehrdad Agha Mohammad Ali Kermani, Mehrdad Maghsoudi, Elmira Darzi

Efficient procurement processes are critical for successful project execution in the construction industry. This study applies process mining techniques to analyze the procurement process of a project-oriented construction organization using the Behfalab platform. By leveraging real event log data extracted from the company’s software system, the research aims to discover the actual procurement process execution, identify bottlenecks and deviations from the ideal process model, and propose data-driven improvement recommendations. The analysis reveals the predominant “happy path” followed in approximately 23.5% of cases examined during a 4.5-month period. It also uncovers the distribution of cases across various procurement activities, highlighting resource-intensive areas such as request handling, technical evaluations, and cost registration. Notably, significant bottlenecks are identified, including lengthy durations for tasks like purchase order processing on credit (average 12.8 working days), inquiry examination by workshop heads (11.6 working days), and cost registration (7.8 working days). Based on the findings and inputs from organizational experts, recommendations are proposed to enhance the procurement process. These include strengthening planning and resource allocation, establishing an authorized vendor list, promoting system thinking and software utilization, enhancing systematic cost registration and control mechanisms, and reinforcing the central procurement unit. The study contributes to the business process management domain by demonstrating the practical application of process mining for optimizing critical processes within project-oriented construction organizations.

Ronnie de Souza Santos, Brody Stuart-Verner, Cleyton Magalhaes

Diversity is an essential aspect of software development because technology influences almost every aspect of modern society, and if the software industry lacks diversity, software products might unintentionally constrain groups of individuals instead of promoting an equalitarian experience to all. In this study, we investigate the perspectives of transgender software professionals about a career in software engineering as one of the aspects of diversity in the software industry. Our findings demonstrate that, on the one hand, trans people choose careers in software engineering for two primary reasons: a) even though software development environments are not exempt from discrimination, the software industry is safer than other industries for transgenders; b) trans people occasionally have to deal with gender dysphoria, anxiety, and fear of judgment, and the work flexibility offered by software companies allow them to cope with these issues more efficiently.

Naresh Kshetri, Mir Mehedi Rahman, Sayed Abu Sayeed et al.

In the banking industry, ransomware is a well-known threat, but since the beginning of 2022, cryptojacking, an emerging threat is posing a considerable challenge to the banking industry. Ransomware has variants, and the attackers keep changing the nature of these variants. This review paper studies the complex background of these two threats and scrutinizes the actual challenges, and problems that the banking industry and financial institutions face. These threats, though distinct in nature, share commonalities, such as financial motivations and sophisticated techniques. We focus on examining the newly emerged variants of ransomware while we provide a comprehensive idea of cryptojacking and its nature. This paper involves a detailed breakdown of the specific threats posed by cryptojacking and ransomware. It explores the techniques cybercriminals use, the variabilities they look for, and the potential consequences for financial institutions and their customers. This paper also finds out how cybercriminals change their techniques following the security upgrades, and why financial firms including banks need to be proactive about cyber threats. Additionally, this paper reviews the background study of some existing papers, finds the research gaps that need to be addressed, and provides suggestions including a conclusion and future scope on those disputes. Lastly, we introduce a Digital Forensics and Incident Response (DFIR) approach for up-to-date cyber threat hunting processes for minimizing both cryptojacking and ransomware attacks in the banking industry.

Florian Stadtman, Adil Rasheed, Trond Kvamsdal et al.

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

Tarunamulia, Jesmond Sammut

In brackishwater aquaculture, pond engineering is essential to meet the cultured species' bio-physical requirements and potentially minimizing social and environmental impacts. This study evaluated the suitability of pond engineering designs and pond, dyke, and canal construction at family-run, extensive brackishwater aquaculture farms in Barru regency of South Sulawesi, Indonesia. Soil properties, topography, hydrological data, field surveys, and high resolution (0.6 m) Quickbird imagery were used to assess the suitability of local pond engineering designs and the efficiency of canals. The study shows that in 752 of the evaluated pond units (430 ha), about 70% of pond beds were not constructed at the optimal pond elevation. Basic engineering requirements for pond layout and dyke and canal designs were not addressed in more than 70% of the pond units. Cease-to-flow conditions commonly occur due to the sedimentation of canals. Flows are also attenuated due to collapsed dykes. Farmers disregarded soil limitations and the impacts of tidal conditions and wave regimes. It is unlikely that shrimp and fish production in the region can increase without significant improvements in pond engineering. The problems identified by the study are not unique to Barru; they also occur in other extensive aquaculture areas in Indonesia and the region. Increased awareness of the need for improved pond engineering techniques is urgently needed to safeguard the economic and environmental sustainability of Indonesia's valuable aquaculture industry.

Cao Jingjing , Lei Ahui , Liu Qing et al.

The shipping industry, which undertakes over 90% of global trade and transportation, has rich digital scenarios. Empowering the shipping industry through smart port construction and integrating ports with virtual–real integration to form a smart port digital base has become an industry consensus. This study defines the smart ports and the virtual – real integration technology system, and analyzes the demand of port operation for virtual – reality integration technology from five aspects: operation planning, activity implementation, equipment operation and maintenance, safety and emergency response, and energy conservation. It also summarizes maps of six virtual – real integration technologies and their application scenarios in smart ports, the technologies being system simulation, extended reality, information-physical system, digital twin, parallel system, and metaverse. After sorting out the development idea and examining scientific issues, a system framework is proposed for the high-quality development of smart ports driven by virtual–real integration technologies, covering the development positioning of technology systems, key technology development directions, and research directions in key fields. Suggestions for development are proposed from the perspectives of deepening application, policy incentives, technological demonstration, and talent cultivation, thereby providing a reference for the engineering application of the virtual–real integration technology system in smart ports.

Andrey V. Bondarenko, Konstantin V. Mushovets, Sergey V. Porshnev et al.

The paper is devoted to a complex analysis of the current system of regulations in the field of security of critical information infrastructure (CII) facilities of the Russian Federation from the point of view of the logic of formation of the legal basis and the chronology of their creation, the results of which have provided a systematic regulatory framework for the security of CII facilities. The main directions of legislative activity in the field of security CII of the Russian Federation have been highlighted and a classification of the current legal acts in terms of it’s requirements has been proposed..The evolution of the content of the regulatory system to ensure the security of significant CII facilities has been described. The results of the analysis led to the conclusion that the state and regulators in the field of IS has developed a sufficient regulatory framework that defines the basic rules, procedures and requirements for the process of categorization, monitoring of its results, as well as providing information security of significant CII facilities. At the same time, on the basis of the experience of categorization of significant objects of the gas industry by the heat and power complex of the Russian Federation, a hypothesis has been made that the establishment of the information security system at specific significant CII sites (e.g., a variety of types of CII objects and areas of activity of CII entities) will require not only the application of existing legal instruments, but also the development of existing sectoral methodical documents in the field of categorization of objects of CII and in the field of construction of the information security system, taking into account their sectoral characteristics.