Hasil untuk "Construction industry"

Qian Wang, Min-koo Kim

Abstract 3D point cloud data obtained from laser scans, images, and videos are able to provide accurate and fast records of the 3D geometries of construction-related objects. Thus, the construction industry has been using point cloud data for a variety of purposes including 3D model reconstruction, geometry quality inspection, construction progress tracking, etc. Although a number of studies have been reported on applying point cloud data for the construction industry in the recent decades, there has not been any systematic review that summaries these applications and points out the research gaps and future research directions. This paper, therefore, aims to provide a thorough review on the applications of 3D point cloud data in the construction industry and to provide recommendations on future research directions in this area. A total of 197 research papers were collected in this study through a two-fold literature search, which were published within a fifteen-year period from 2004 to 2018. Based on the collected papers, applications of 3D point cloud data in the construction industry are reviewed according to three categories including (1) 3D model reconstruction, (2) geometry quality inspection, and (3) other applications. Following the literature review, this paper discusses on the acquisition and processing of point cloud data, particularly focusing on how to properly perform data acquisition and processing to fulfill the needs of the intended construction applications. Specifically, the determination of required point cloud data quality and the determination of data acquisition parameters are discussed with regard to data acquisition, and the extraction and utilization of semantic information and the platforms for data visualization and processing are discussed with regard to data processing. Based on the review of applications and the following discussions, research gaps and future research directions are recommended including (1) application-oriented data acquisition, (2) semantic enrichment for as-is BIM, (3) geometry quality inspection in fabrication phase, and (4) real-time visualization and processing.

N. Onat, M. Kucukvar

Abstract This paper conducts a global review and a macro-level supply chain analysis focusing on carbon footprint of construction industry worldwide for the period between 2009 and 2020 using the Scopus database. A total of 1833 journal articles are revealed with focus on carbon footprint in the field of construction in general, of which only 115 (6% of the total) studies have a macro-level analysis of the construction sector, providing a more holistic overview of the construction sector from various aspects. These macro-level studies were reviewed and classified based on journal, country, year, method, scope of analysis, type of construction, and period. The findings showed that approximately 60% of these studies focus on the Chinese construction industry and the majority of studies analyzed national-level (75%) and city-level (18%) carbon footprints of construction. On the contrary, global-level analysis has a lower share, which accounted for only 6% of reviewed articles. The review showed that more than 20% of studies use the input-output analysis as the main methodological approach to quantify macro-level carbon emission from construction sector, which is followed by the process-based life cycle assessment with 10% share, where more bottom-up approaches are employed. There are only a handful of articles found in the literature using a hybrid life cycle assessment and global multiregional input-output analysis for carbon footprint accounting of construction. Furthermore, there is also no study found in the literature, which presented a comprehensive regional and global supply chain analysis of construction carbon footprints. The results revealed that the largest portion of carbon emissions stem from the regional and global supply chains of the construction industries. The authors concluded that carbon reduction policies should not only consider the limited regional impacts; however, it must take into account the role of indirect, complex and interconnected global supply chains of construction industries.

Marios Stanitsas, K. Kirytopoulos, V. Leopoulos

Abstract Sustainability concepts showcase significant value in construction projects. The discipline of project management is also integrating sustainability issues into its approaches. Under this notion, this study explores the integration of sustainability indicators into project management practices of construction projects. Current literature discloses many indicators/key factors as contributing towards the sustainability success of construction projects. However, the lack of an all-encompassing categorization creates difficulties in directing project managers towards their proper utilization. This paper aims to contribute towards the holistic view of sustainability in project management, especially for construction projects. A systematic literature review was conducted towards the understanding of the key topics and the findings were validated through semi-structured interviews. Eighty-two (82) sustainability indicators related to project management practices in construction projects were finally identified. Their categorization into economic, environmental and social/management sustainability indicators was completed through semi-structured interviews with construction experts and via previous literature analysis. The economic related indicators finalized in 27; 18 for the environmental dimension and 37 indicators were included in the social/management dimension. This study contributes to research on sustainable project management for construction projects in two main ways: (1) it provides a holistic view of sustainable project management indicators, covering the full spectrum of the triple constraint (TBL); (2) it offers the possibility for practitioners to choose the right mix of indicators, depending on the sustainability focus they want to provide in their projects.

Chris Newman, D. Edwards, Igor Martek et al.

Purpose: Industry 4.0 is predicted to be a game-changer, revolutionizing commercial and manufacturing practices through improved knowledge utilization and efficiencies. The barriers however, are significant, and the construction industry remains notoriously slow to take up innovations. This study reviews the research work in Industry 4.0 as it relates to construction, and examines a leading UK-based construction firm to ascertain the prognosis for Industry 4.0 roll-out in terms of the impediments and opportunities. Methodology: A multistage mixed philosophies and methods approach was adopted for this study. First, an interpretivist epistemological lens was used to synthesise extant literature as a means of contextualising the present study. Second, an empirical case study using a post-positivist stance and inductive reasoning was conducted to explore practitioner acceptance of Industry 4.0 in the UK construction context. Findings: Findings from the literature review indicate studies in Industry 4.0 to be a relatively new phenomenon, with developed countries and Germany in particular leading in the field. The range of opportunities are many, but so too are the barriers to enablement. Findings from the case study provide real-world corroboration of the review; practitioners are sanguine about Industry 4.0’s potential to reinvigorate the construction industry, but also note that implementation remains curtailed by residual managerial practices dependent on ‘human interaction.’ At present, much of the focus of industry practitioners is on the implementation of Building Information Modelling (BIM), often at the expense of other more advanced technologies within Industry 4.0. Originality: Research in Industry 4.0 is limited, with the emphasis being on technology application. This paper, by contrast, maps the totality of work carried out so far and presents an assessment of Industry 4.0’s progression, potential and degree of uptake within the UK construction industry.

Jie Xiong, Degui Wang, Liping Xie et al.

The construction of mass concrete foundations for nuclear power plants faces significant challenges in controlling hydration heat and preventing early-age thermal cracking. This study develops an integrated framework combining high-fidelity thermal–mechanical simulation, real-time temperature monitoring, and construction process optimization to address these issues. Focusing on the VVER-1200 reactor raft foundation in the Xudapu NPP Phase II Project, an innovative center-to-periphery synchronous pouring method is proposed, departing from conventional inclined or layered pouring by strategically utilizing stage time lags to moderate the radial temperature gradient. Numerical simulations demonstrate that this method significantly reduces the peak temperature and thermal stress. Field validation shows that the maximum core-to-surface temperature difference is controlled within 19.8 °C, well below the critical threshold of 25 °C, and the peak concrete temperature remains at 66.7 °C, safely below the risk level for delayed ettringite formation (82–85 °C). The cracking risk coefficient K remains below 0.65, indicating a low probability of thermal cracking. Post-construction inspection confirms the absence of thermal cracks in the 5240 m³ monolithic pour. The proposed methodology offers a reliable, science-based approach for thermal crack mitigation and serves as a valuable reference for similar large-scale mass concrete structures in nuclear and other critical infrastructure projects.

Suyash Mishra, Qiang Li, Srikanth Patil et al.

Vision Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200,000 PDFs, 25,326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25,326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3-8 times efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.

Y. Labaran, V. S. Mathur, Shehu Usman Muhammad et al.

Jingxiao Zhang, You Ouyang, P. Ballesteros‐Pérez et al.

Abstract In the current environmentally constrained context, deploying effective environmental regulations (ERs) to promote greener technologies is necessary. Green technology innovation efficiency (GTIE) reflects the efficiency of an industry’s use of resources in the green technology innovation process. However, previous research has considered innovation as a black box regarding the potential contribution and diversity of ERs. In order to analyze the differential impacts of ERs on GTIE, this study classifies ERs into command-and-control, market-based and voluntary. By adopting China's 2000-2017 construction industry as a case study, this study analyzes GTIE evolution based on a network Epsilon Based Measure (EBM) model and analyze the impacts of ERs by Tobit Regression. Findings suggest that: (1) There is a significant disconnection between the Research & Development (R&D) and commercial application stages of green technology in construction industry. The construction industry is able to turn most R&D achievements into profits at the commercialization stage, but a large amount of R&D investment does not produce R&D achievements. (2) Different types of ERs have different impacts on GTIE, but their intended outcomes can only be achieved by a suitable combination of them.

Yaser Gamil, Abdulsalam Alhagar

Since the World Health Organization (WHO) announced the coronavirus 2019 (COVID-19) outbreak as a pandemic, many countries have declared a complete national lockdown after a remarkable spike in COVID 19 cases. These decisions have restricted the movement of people and resulted in a complete shutdown of many businesses across many sectors. The construction industry, as a significant growth driver of the economy with no exception, has also been completely shut down. All the developments and projects were postponed until further notice. It is, therefore, a prudent to address the impact of the pandemic at the outset and end of the crisis to prepare for any future possibility and gain lessons for plans. This study aims to investigate the effect of COVID 19 on the construction industry's survival. The impacts and fallout have been determined and evaluated through the recruitment of construction experts and practitioners. The impacts have been classified into different groups which include economic, human resources. The study implied two methods include exploratory interviews and questionnaire surveys. The study found the most prominent impacts of COVID 19 are the suspension of projects, labour impact and job loss, time overrun, cost overrun, and financial implications. The findings of this study shed light on the consequences of the sudden occurrence of pandemic and raise awareness of the most critical impacts which can’t be overlooked. The findings also help project stakeholders to realise the sequences of the sudden epidemic and prepare for the worst-case scenario during the planning stage of the construction projects.

Ahmad M.Zamil, Mohammad Alhusban, Alharkan Abdulrahman

Abstract This research rigorously examines the implementation challenges of value management (VM) implementation challenges within construction projects across Jordan. This study is guided by a conceptual framework linking organisational factors to VM implementation barriers, highlighting the impact on project outcomes and providing a basis for targeted interventions in developing sustainable construction. Utilizing a comprehensive survey, insights were gathered from 103 industry experts, and the collected data were rigorously analyzed through exploratory factor analysis (EFA) and partial least squares structural modeling (PLS-SEM). The analysis identified four critical constructs of VM implementation challenges: culture, awareness, resources, and policy. The analysis identified four important constructs of VM adoption challenges: culture, awareness, resources, and policy. Among these constructs, culture appeared to be the most important barrier (path-coefficient = 0.317), followed by resources (0.285), awareness (0.271), and policy (0.209). The findings not only illuminate the complex dynamics within Jordan's construction sector but also offer valuable implications for other developing nations with comparable socio-economic and environmental backgrounds, struggling with similar challenges. This study contributes substantially to enhancing the understanding among stakeholders of the barriers to effective VM implementation and proposes actionable strategies to mitigate these challenges, ultimately aiming to improve construction quality and cost-efficiency in developing contexts. However, this research is limited to Jordan; thus, future studies covering a broader geographical scope are recommended.

Yenus Muhammed Argaw, Yingqi Liu

Small and medium-scale enterprises (SMEs) play a crucial role in most developing countries. However, many of these enterprises face a range of challenges that hinder their growth and survival. This research aimed to examine the critical success factors affecting the growth of different sizes of SMEs in the Ethiopian construction industry. The novelty of the paper lies in addressing the identification problem found in the existing literature by employing appropriate estimation techniques to quantify the contribution of multiple success factors, thereby moving beyond descriptive statistics. Using an ordered logit model, we obtained interesting and informative results about the effects of critical success factors on the growth of SMEs. The results invariably suggest that the contribution of the critical success factors depends on the firm's specific factors (annual turnover). We found that not all factors exert comparable effects on success; certain factors wield a substantial influence for larger SMEs, whereas others demonstrate a significant impact on smaller SMEs. The gender of the owner has a positive and significant impact on the sustainable growth and success of Ethiopian construction firms with an annual turnover below 500,000 Birr. In contrast, for firms with an annual turnover above 500,000 Birr, the entrepreneur’s education level and entrepreneurial orientation are strongly and positively associated with firm success, showing a greater influence compared to lower-turnover firms. Therefore, this paper strongly recommends SME owners, managers, and staffs consider the level of annual turnover as the effectiveness of success factors depends on the level of annual turnover.

Yi-Hsuan Lin, Lalitphat Khongsomchit, Sakdirat Kaewunruen et al.

IntroductionBuilding Information Modelling (BIM) has emerged as a multidisciplinary methodology that integrates information-rich data with virtual representations to support the management of built assets throughout their lifecycle. While BIM is increasingly adopted in architecture, engineering, and construction (AEC) industries and demonstrates significant value in infrastructure projects; however, its application in the railway sector remains limited. The complexity of railway networks, combined with the growing demand for transit projects, presents unique challenges that hinder effective implementation.MethodsThis study investigates the barriers of BIM adoption within the railway industry through a structured questionnaire distributed to professionals and a subsequent detailed analysis of responses.ResultsThis study identifies critical gaps in current BIM practices and highlights several severe obstacles that require urgent attention. Feedback reveals key challenges across four main areas: (1) Technology, (2) Market, (3) Socio-cultural factors, and (4) Policy.DiscussionBy outlining these barriers and suggesting potential solutions, the study provides valuable insights for stakeholders and identifies future research directions to advance BIM integration in railway projects.

Martyna Zemlik, Beata Białobrzeska, Mateusz Stachowicz et al.

As a result of welding processes in boron-alloyed martensitic armor steels, unfavorable microstructural changes occur, leading to a significant reduction in the mechanical properties of both the weld metal and the base material. The dendritic structure of the weld metal and the partial tempering in the heat-affected zone contribute to the decreased durability of structural components, thereby deteriorating their performance. This issue is particularly important since such steels are widely used not only in the defense industry but also in the mining, construction, transportation, and metallurgical sectors, where they operate under conditions of intensive abrasive wear. For this reason, the authors attempted to improve the mechanical properties of welded joints of boron-alloyed martensitic armor steel (with a nominal hardness of 500 HBW) through post-weld heat treatment. The welded joint was evaluated based on metallographic examinations using light microscopy and scanning electron microscopy, as well as abrasive wear tests carried out on a T-07 tribotester. The conducted investigations demonstrated that, under loose abrasive conditions (using electrofused alumina), heat treatment increased the wear resistance of the joints by 55% compared to the as-welded condition. The obtained results were compared with selected grades of Hardox steel commonly used in industrial applications.

Keliang Chen, Bo Chen, Wanqing Chen

High-quality growth of the construction industry and an improved human settlements environment are essential to sustainable urbanization. Existing studies have paid limited systematic attention to the spatial and temporal dynamics of the coordinated development between the construction industry and human settlements, as well as the underlying factors driving regional disparities. This gap restricts the formulation of precise, differentiated sustainable policies tailored to regions at different development stages and with varying resource endowments. Southern China, characterized by pronounced spatial heterogeneity and unique development trends, offers a natural laboratory for examining the spatio-temporal interaction between these two dimensions. Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. The main findings are as follows. (1) The coupling coordination degree rose steadily, forming a stepped spatial pattern from the southwest through the center to the southeast. (2) The coupling coordination degree appears obvious polarization effect, presenting a spatial linkage pattern with Jiangsu-Shanghai-Zhejiang, Hubei-Hunan-Jiangxi, and Sichuan-Chongqing as the core of the three major clusters. (3) The overall Dagum Gini coefficient declined, but intra-regional disparities persisted: values were highest in the southeast, moderate in the center, and lowest in the southwest; inter-regional differences dominated the total inequality. (4) Forecasts for 2023–2027 suggest further improvement in the coupling coordination degree, yet spatial divergence will widen, creating a configuration of “eastern leadership, central catch-up acceleration, and differentiated southwestern development.” This study provides an evidence base for policies that foster high-quality construction sector growth and enhance the living environment. The findings of this study indicate that policymaking should prioritize promoting synergistic regional development, enhancing the radiating and driving role of core regions, and establishing a multi-level coordinated governance mechanism to bridge regional disparities and foster more balanced and sustainable development.

Khoo Terh Jing, Chin Yee Ha , Mohd Wira Mohd Shafiei et al.

This research explores the current green site management practices (GSMPs) commonly practiced by contractors. While the construction industry has contributed significantly to Malaysia’s development, it has also raised various environmental concerns. GSMPs are gaining attention as a solution to these issues. Nevertheless, their implementation faces various challenges, such as financial concerns and a lack of knowledge. A qualitative approach was adopted to focus on contractors’ experiences with green practices. Five contractors were randomly selected from the construction sites in Malaysia using the convenience sampling method. These respondents, all at the management level, were well-positioned to provide insights. Data collection continued until no new issues emerged from the interviews. The findings revealed that GSMPs are becoming a current trend within the industry. Contractors are beginning to integrate green practices in their construction activities, focusing on construction site waste management, workforce management, best regulatory practices, site establishment and administration, and site facilities. However, there is a notable lack of awareness and knowledge about these green practices among contractors. The study offers practical implications for the future of GSMPs, highlighting the need for increased understanding and adoption. By elaborating on available practices and their implications, this study aims to encourage broader implementation of GSMPs in construction sites.

Parul Khanna, Sameer Prabhu, Ramin Karim et al.

The construction industry is presently going through a transformation led by adopting digital technologies that leverage Artificial Intelligence (AI). These industrial AI solutions assist in various phases of the construction process, including planning, design, production and management. In particular, the production phase offers unique potential for the integration of such AI-based solutions. These AI-based solutions assist site managers, project engineers, coordinators and other key roles in making final decisions. To facilitate the decision-making process in the production phase of construction through a human-centric AI-based solution, it is important to understand the needs and challenges faced by the end users who interact with these AI-based solutions to enhance the effectiveness and usability of these systems. Without this understanding, the potential usage of these AI-based solutions may be limited. Hence, the purpose of this research study is to explore, identify and describe the key factors crucial for developing AI solutions in the construction industry. This study further identifies the correlation between these key factors. This was done by developing a demonstrator and collecting quantifiable feedback through a questionnaire targeting the end users, such as site managers and construction professionals. This research study will offer insights into developing and improving these industrial AI solutions, focusing on Human-System Interaction aspects to enhance decision support, usability, and overall AI solution adoption.

Abdalwhab Abdalwhab, Ali Imran, Sina Heydarian et al.

The construction industry has long explored robotics and computer vision, yet their deployment on construction sites remains very limited. These technologies have the potential to revolutionize traditional workflows by enhancing accuracy, efficiency, and safety in construction management. Ground robots equipped with advanced vision systems could automate tasks such as monitoring mechanical, electrical, and plumbing (MEP) systems. The present research evaluates the applicability of open-vocabulary vision-language models compared to fine-tuned, lightweight, closed-set object detectors for detecting MEP components using a mobile ground robotic platform. A dataset collected with cameras mounted on a ground robot was manually annotated and analyzed to compare model performance. The results demonstrate that, despite the versatility of vision-language models, fine-tuned lightweight models still largely outperform them in specialized environments and for domain-specific tasks.

Lujun Li, Lama Sleem, Niccolo' Gentile et al.

With the emergence of ChatGPT, Transformer models have significantly advanced text classification and related tasks. Decoder-only models such as Llama exhibit strong performance and flexibility, yet they suffer from inefficiency on inference due to token-by-token generation, and their effectiveness in text classification tasks heavily depends on prompt quality. Moreover, their substantial GPU resource requirements often limit widespread adoption. Thus, the question of whether smaller language models are capable of effectively handling text classification tasks emerges as a topic of significant interest. However, the selection of appropriate models and methodologies remains largely underexplored. In this paper, we conduct a comprehensive evaluation of prompt engineering and supervised fine-tuning methods for transformer-based text classification. Specifically, we focus on practical industrial scenarios, including email classification, legal document categorization, and the classification of extremely long academic texts. We examine the strengths and limitations of smaller models, with particular attention to both their performance and their efficiency in Video Random-Access Memory (VRAM) utilization, thereby providing valuable insights for the local deployment and application of compact models in industrial settings.

Emre Girgin, Arda Taha Candan, Coşkun Anıl Zaman

The fields of autonomous systems and robotics are receiving considerable attention in civil applications such as construction, logistics, and firefighting. Nevertheless, the widespread adoption of these technologies is hindered by the necessity for robust processing units to run AI models. Edge-AI solutions offer considerable promise, enabling low-power, cost-effective robotics that can automate civil services, improve safety, and enhance sustainability. This paper presents a novel Edge-AI-enabled drone-based surveillance system for autonomous multi-robot operations at construction sites. Our system integrates a lightweight MCU-based object detection model within a custom-built UAV platform and a 5G-enabled multi-agent coordination infrastructure. We specifically target the real-time obstacle detection and dynamic path planning problem in construction environments, providing a comprehensive dataset specifically created for MCU-based edge applications. Field experiments demonstrate practical viability and identify optimal operational parameters, highlighting our approach's scalability and computational efficiency advantages compared to existing UAV solutions. The present and future roles of autonomous vehicles on construction sites are also discussed, as well as the effectiveness of edge-AI solutions. We share our dataset publicly at github.com/egirgin/storaige-b950

D. Scott, Tim Broyd, Ling Ma

First academic publications on blockchain in construction instantiated in 2017, with three documents. Over the course of several years, new literature emerged at an average annual growth rate of 184%, surmounting to 121 documents at time of writing this article in early 2021. All 121 publications were reviewed to investigate the expansion and progression of the topic. A mixed methods approach was implemented to assess the existing environment through a literature review and scientometric analysis. Altogether, 33 application categories of blockchain in construction were identified and organised into seven subject areas, these include (1) procurement and supply chain, (2) design and construction, (3) operations and life cycle, (4) smart cities, (5) intelligent systems, (6) energy and carbon footprint, and (7) decentralised organisations. Limitations included using only one scientific database (Scopus), this was due to format inconsistencies when downloading and merging various bibliographic data sets for use in visual mapping software.