Hasil untuk "Construction industry"

Kamyar Kabirifar, M. Mojtahedi, C. Wang et al.

Abstract Construction and demolition waste (C&DW) as a direct consequence of rapid urbanization is increasing around the world. C&DW generation has been identified as one of the major issues in the construction industry due to its direct impacts on the environment as well as the efficiency of construction industry. It is estimated that an overall of 35% of C&DW is landfilled globally, therefore, effective C&DW management is crucial in order to minimize detrimental impacts of C&DW for the environment. As the industry cannot continue to practice if the resources on which it depends are depleted, C&DW management needs to be implemented in an effective way. Despite considering many well-developed strategies for C&DW management, the outputs of the implementation of these strategies is far from optimum. The main reason of this inefficiency is due to inadequate understanding of principal factors, which play a vital role in C&DW management. Therefore, the aim of this research is to critically scrutinize the concept of C&DW and its managerial issues in a systematic way to come up with the effective C&DW management. In order to achieve this aim, and based on a systematic review of 97 research papers relevant to effective C&DW management, this research considers two main categories as fundamental factors affecting C&DW management namely, C&DW management hierarchy including reduce, reuse, and recycle strategies, and effective C&DW management contributing factors, including C&DW management from sustainability perspective, C&DW stakeholders’ attitudes, C&DW project life cycle, and C&DW management tools. Subsequently, these factors are discussed in detail and findings are scrutinized in order to clarify current and future practices of C&DW management from both academic and practical perspectives.

A. Dubois, Lars-Erik Gadde

S. Prieto, Eyob Mengiste, Borja García de Soto

Generative Pre-Trained Transformer (GPT) language models such as ChatGPT have the potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This paper presents a study in which ChatGPT was used to generate a construction schedule for a simple construction project. The output from ChatGPT was evaluated by a pool of participants that provided feedback regarding their overall interaction experience and the quality of the output. The results show that ChatGPT can generate a coherent schedule that follows a logical approach to fulfill the requirements of the scope indicated. The participants had an overall positive interaction experience and indicated the potential of such a tool in automating many preliminary and time-consuming tasks. However, the technology still has limitations, and further development is needed before it can be widely adopted in the industry. Overall, this study highlights the advantages of using large language models and Natural Language Processing (NLP) techniques in the construction industry and the need for further research.

S. Barbhuiya, B. B. Das

This review paper presents a comprehensive analysis of Life Cycle Assessment (LCA) methodologies applied to construction materials. It begins with an introduction highlighting the significance of LCA in the construction industry, followed by an overview of LCA principles, phases and key parameters specific to construction materials. The methodological approaches utilised in LCA, including inventory analysis, impact assessment, normalisation, allocation methods and uncertainty analysis, are discussed in detail. The paper then provides a thorough review of LCA studies on various construction materials, such as cement, concrete, steel and wood, examining their life cycle stages and environmental considerations. The review also explores recent advances in LCA for construction materials, including circular economy principles, renewable alternatives, technological innovations and policy implications. The challenges and future directions in LCA implementation for construction materials are discussed, emphasising the need for data quality, standardisation, social aspects integration and industry-research collaboration. The provides valuable insights for researchers, policymakers and industry professionals to enhance sustainability in the construction sector through informed decision-making based on LCA.

S. Sepasgozar, A. Khan, K. Smith et al.

The construction industry is slow to adopt new technologies. The implementation of digital technologies and remote operations using robots were considered farfetched affairs and unbelievable approaches. However, the effect of COVID-19 on clients and construction companies put high pressure on construction managers to seek digital solutions and justified the need for remote operating or distant controlling technologies. This paper aims to investigate the state of play in construction technology implementation and presents a roadmap for developing and implementing required technologies for the construction industry. The COVID-19 disruption required new methods of working safely and remotely and coincided with the advent of advanced automation and autonomous technologies. This paper aims to identify gaps and 11 disruptive technologies that may lead to upheaval and transformation of the construction sector, perhaps in this decade. A road map for technology implementation can be helpful in developing business strategies at the organizational level as a theoretical measure, and it can facilitate the technology implementation process at the industry level as a practical measure. The roadmap can be used as a framework for policymakers to set industry or company strategies for the next 10 years (2030).

Hailiang Zhao, Ziqi Wang, Daojiang Hu et al.

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

Peter Hines, Florian Magnani, Josefa Mula et al.

This paper proposes a human-centered conceptual model integrating lean and Industry 4.0 based on the literature review and validated it through a case study in the context of an advanced automotive first-tier supplier. Addressing a significant gap in existing research on lean Industry 4.0 implementations, the study provides both theoretical insights and practical findings. It emphasizes the importance of a human-centered approach, identifies key enablers and barriers. In the implementation process of the case study, it is considered at group level and model site level through operational, social and technological perspectives in a five-phase multi-method approach. It shows what effective human-centered lean Industry 4.0 implementation look like and how advanced lean tools can be digitized. It highlights 26 positive and 10 negative aspects of the case and their causal relation. With the appropriate internal and external technological knowhow and people skills, it shows how successful implementation can benefit the organization and employees based on the conceptual model that serves as a first step toward lean Industry 5.0.

Sheikh Muhammad Farjad

DaemonSec is an early-stage startup exploring machine learning (ML)-based security for Linux daemons, a critical yet often overlooked attack surface. While daemon security remains underexplored, conventional defenses struggle against adaptive threats and zero-day exploits. To assess the perspectives of IT professionals on ML-driven daemon protection, a systematic interview study based on semi-structured interviews was conducted with 22 professionals from industry and academia. The study evaluates adoption, feasibility, and trust in ML-based security solutions. While participants recognized the potential of ML for real-time anomaly detection, findings reveal skepticism toward full automation, limited security awareness among non-security roles, and concerns about patching delays creating attack windows. This paper presents the methods, key findings, and implications for advancing ML-driven daemon security in industry.

A. R. Pina, Shams El-Adawy, H. J. Lewandowski et al.

Continued growth of the quantum information science and engineering (QISE) industry has resulted in stakeholders spanning education, industry, and government seeking to better understand the workforce needs. This report presents a framework for the categorization of roles in the QISE industry based on 42 interviews of QISE professionals across 23 companies, as well as a description of the method used in the creation of this framework. The data included information on over 80 positions, which we have grouped into 29 roles spanning four primary categories. For each primary category we provide an overview of what unites the roles within a category, a description of relevant subcategories, and definitions of the individual roles. These roles serve as the basis upon which we generate profiles of these roles, which include information about role critical tasks, necessary knowledge and skills, and educational requirements. Our next report will present such profiles for each of the roles presented herein.

Bo Wu

In order to explore whether environmental liability insurance has an important impact on industrial emission reduction, this paper selects provincial (city) level panel data from 2010 to 2020 and constructs a two-way fixed effect model to analyze the impact of environmental liability insurance on carbon emissions from both direct and indirect levels. The empirical analysis results show that: at the direct level, the development of environmental liability insurance has the effect of reducing industrial carbon emissions, and its effect is heterogeneous. At the indirect level, the role of environmental liability insurance is weaker in areas with developed financial industry and underdeveloped financial industry. Further heterogeneity analysis shows that in the industrial developed areas, the effect of environmental liability insurance on carbon emissions is more obvious. Based on this, countermeasures and suggestions are put forward from the aspects of expanding the coverage of environmental liability insurance, innovating the development of environmental liability insurance and improving the level of industrialization.

Joon-Soo Kim

The construction industry, particularly in road projects, faces pressing challenges related to environmental sustainability and cost management. As road construction contributes significantly to environmental degradation and demands large-scale investments, there is an urgent need for innovative solutions that balance environmental impact with economic feasibility. Despite advancements in building technologies and energy-efficient materials, accurate and reliable predictions for environmental load and construction costs during the planning and design stages remain limited due to insufficient data systems and complex project variables. This study explores the application of machine-learning techniques to predict environmental loads and construction costs in road projects, using a dataset of 100 national road construction cases in the Republic of Korea. The research employs multiple regression analysis, regression tree models, and case-based reasoning (CBR) to estimate these critical parameters at both the planning and design stages. A novel aspect of this research lies in its comparative analysis of different machine-learning models to address the challenge of limited and non-ideal data environments, offering valuable insights for enhancing predictive accuracy despite data scarcity. The results reveal that while regression models perform better in the design stage, achieving error rates of 12% for environmental load estimation and 23% for construction costs, the case-based reasoning model outperforms others in the planning stage, with a 15.9% average error rate for environmental load and 19.9% for construction costs. These findings highlight the potential of machine-learning techniques to drive environmentally conscious and economically sound decision-making in construction, despite data limitations. However, the study also identifies the need for larger, more diverse datasets and better integration of qualitative data to improve model accuracy, offering a roadmap for future research in sustainable construction management.

Xiaochun Zheng, Gaoqian Zhang, Kejun Wei et al.

Balanced polyunsaturated fatty acids (PUFAs) ratio, nutritional value and flavor are key edible oil attributes. Based on pressure technology, mixing ratio equations, response surface methodology, and analyses of physicochemical and nutritional indexes as well as key volatiles, this study explored oilseed mixing ratio, optimal extraction conditions in the blended cold pressing (BCP) process, and blended oil's physicochemical properties, nutritional profile, and aroma. Results showed the optimal flaxseed:safflower seed mass ratio was 1:(1.66–2.6); best extraction conditions: 64 °C pressing temperature, 6.7 % moisture, 1.5-min 800 W microwave treatment, with a final oil yield of 19.83 %. BCP oil (BCPO) showed enhanced oxidative stability, improved nutritional functions, and richer volatiles (aldehydes, alcohols, pyrazines). Sensory evaluation showed BCPO had prominent vegetable, roasted and overall flavors. This study developed a one-step BCP process to prepare blended oils with balanced PUFAs, providing a theoretical basis for BCP application in edible oil processing.

Patrick Schmidt, Lazaros Nalpantidis

This paper provides a dataset of 14,805 RGB images with segmentation labels for autonomous robotic inspection of reinforced concrete defects. Baselines for the YOLOv8L-seg, DeepLabV3, and U-Net segmentation models are established. Labelling inconsistencies are addressed statistically, and their influence on model performance is analyzed. An error identification tool is employed to examine the error modes of the models. The paper demonstrates that YOLOv8L-seg performs best, achieving a validation mIOU score of up to 0.59. Label inconsistencies were found to have a negligible effect on model performance, while the inclusion of more data improved the performance. False negatives were identified as the primary failure mode. The results highlight the importance of data availability for the performance of deep learning-based models. The lack of publicly available data is identified as a significant contributor to false negatives. To address this, the paper advocates for an increased open-source approach within the construction community.

Lizhen Liang, Han Zhuang, James Zou et al.

Artificial intelligence (AI) has seen fast paced development in industry and academia. However, striking recent advances by industry have stunned the field, inviting a fresh perspective on the role of academic research on this progress. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations produce the most impactful work overall but do not have the novelty level of academic teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the progress of AI.

Ashok Urlana, Charaka Vinayak Kumar, Bala Mallikarjunarao Garlapati et al.

Large language models (LLMs) are playing a pivotal role in deploying strategic use cases across a range of organizations, from large pan-continental companies to emerging startups. The issues and challenges involved in the successful utilization of LLMs can vary significantly depending on the size of the organization. It is important to study and discuss these pertinent issues of LLM adaptation with a focus on the scale of the industrial concerns and brainstorm possible solutions and prospective directions. Such a study has not been prominently featured in the current research literature. In this study, we adopt a threefold strategy: first, we conduct a case study with industry practitioners to formulate the key research questions; second, we examine existing industrial publications to address these questions; and finally, we provide a practical guide for industries to utilize LLMs more efficiently. We release the GitHub\footnote{\url{https://github.com/vinayakcse/IndustrialLLMsPapers}} repository with the most recent papers in the field.

Ágoston Kaposi, Zoltán Kolarovszki, Adrián Solymos et al.

Unitary designs are essential tools in several quantum information protocols. Similarly to other design concepts, unitary designs are mainly used to facilitate averaging over a relevant space, in this case, the unitary group $\mathrm{U}(d)$. While it is known that exact unitary $t$-designs exist for any degree $t$ and dimension $d$, the most appealing type of designs, group designs (in which the elements of the design form a group), can provide at most $3$-designs. Moreover, even group $2$-designs can exist only in limited dimensions. In this paper, we present novel construction methods for creating exact generalized group designs based on the representation theory of the unitary group and its finite subgroups that overcome the $4$-design-barrier of unitary group designs. Furthermore, a construction is presented for creating generalized group $2$-designs in arbitrary dimensions.

Katarzyna Michałowska, Helga Margrete Bodahl Holmestad, Signe Riemer-Sørensen

We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery, which significantly diverge from typical vehicular traffic on established roads. Our proposed method first identifies intersections in the road network that serve as critical decision points, and then connects them with edges to produce a graph, which can subsequently be used for planning and task-allocation. We demonstrate the approach by mapping roads at a real-life construction site in Norway. The method is validated on four increasingly complex segments of the map. In our tests, the method achieved perfect accuracy in detecting intersections and inferring roads in data with no or low noise, while its performance was reduced in areas with significant noise and consistently missing GPS updates.

Srinjoy Mitra, Jean-Pierre Raskin

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.

Panos Fitsilis, Paraskevi Tsoutsa, Vyron Damasiotis et al.

This article analyzes around 200 online articles to identify trends within Industry 5.0 using artificial intelligence techniques. Specifically, it applies algorithms such as LDA, BERTopic, LSA, and K-means, in various configurations, to extract and compare the central themes present in the literature. The results reveal a convergence around a core set of themes while also highlighting that Industry 5.0 spans a wide range of topics. The study concludes that Industry 5.0, as an evolution of Industry 4.0, is a broad concept that lacks a clear definition, making it difficult to focus on and apply effectively. Therefore, for Industry 5.0 to be useful, it needs to be refined and more clearly defined. Furthermore, the findings demonstrate that well-known AI techniques can be effectively utilized for trend identification, particularly when the available literature is extensive and the subject matter lacks precise boundaries. This study showcases the potential of AI in extracting meaningful insights from large and diverse datasets, even in cases where the thematic structure of the domain is not clearly delineated.

Thanon Bualuang, Peerapong Jitsangiam, Korakod Nusit et al.

This study evaluates styrene butadiene rubber (SBR) and styrene acrylic latex (SA) as modifiers in cement-treated subbase materials (CTSB) to enhance mechanical properties and reduce cement usage sustainably. Optimal ratios for stabilizing sub-standard lateritic soils were identified, reducing water demand and increasing mechanical strength in polymer-modified cement pastes. A 10 % SA and a 15 % SBR as cement replacement by mass significantly improved bearing strength and strain capacities in CTSB, signifying enhanced flexibility and elasticity. Despite slight changes in compaction characteristics, the study identified 1.6 % SA and 2.4 % SBR as optimal binder (i.e., polymer-cement mixture) contents, compared to 3.3 % cement for conventional CTSB with similar unconfined compressive strength standards. SBR-enriched CTSB exhibited superior resilient modulus, indicating stronger inter-particle bonding. The integration of SA and SBR reduced capillary rise and enhanced moisture stability. This sustainable approach enhances pavement durability and reduces CO2 emissions by minimizing cement use. The findings emphasize the potential of polymer-modified CTSB for cost-effective and environmentally friendly road construction, offering significant implications for advancing pavement engineering materials and promoting eco-friendly practices within the industry.