Hasil untuk "Construction industry"

M. Ramage, H. Burridge, Marta Busse-Wicher et al.

Trees, and their derivative products, have been used by societies around the world for thousands of years. Contemporary construction of tall buildings from timber, in whole or in part, suggests a growing interest in the potential for building with wood at a scale not previously attainable. As wood is the only significant building material that is grown, we have a natural inclination that building in wood is good for the environment. But under what conditions is this really the case? The environmental benefits of using timber are not straightforward; although it is a natural product, a large amount of energy is used to dry and process it. Much of this can come from the biomass of the tree itself, but that requires investment in plant, which is not always possible in an industry that is widely distributed among many small producers. And what should we build with wood? Are skyscrapers in timber a good use of this natural resource, or are there other aspects of civil and structural engineering, or large-scale infrastructure, that would be a better use of wood? Here, we consider a holistic picture ranging in scale from the science of the cell wall to the engineering and global policies that could maximise forestry and timber construction as a boon to both people and the planet.

H. Rao

N. Kisku, H. Joshi, M. Ansari et al.

H. Thai, T. Ngo, B. Uy

Abstract Modular construction is considered as a game-changing technology since it offers faster construction, safer manufacturing, better quality control, and lower environmental impacts compared with the traditional onsite construction. These benefits can be maximised in high-rise buildings due to their inherently topological modular form and the increased number of repeatable modules. However, current applications of modular construction for high-rise buildings are very limited due to the lack of strong structural systems and joining techniques to ensure structural integrity, overall stability, and robustness of an entirely modular building. In addition, the unavailability of design guidelines also inhibits the construction industry in implementing such technology. With recent advancements in structural systems and materials, there is great potential for real world applications of modular construction in high-rise buildings. This paper presents a critical review of recent innovations in modular construction technology for high-rise buildings with an emphasis on structural systems, joining techniques, progressive collapse and structural robustness. The developments of design codes for modular construction are also discussed. The paper concludes by highlighting the technical challenges that hinder the widespread adoption of modular construction, and proposing potential solutions for future research. This review paper is expected to be a complete reference for experts, researchers and professionals in this field of study.

V. Mechtcherine, V. Nerella, F. Will et al.

Abstract The construction industry faces severe problems resulting from low productivity and increasing shortages of skilled labor. The purposeful digitalization and automation of all relevant stages, from design and planning to the actual construction process appears to be the only feasible solution to master these urgent challenges. Additive concrete construction has a high potential to be a key part of the solution. In the first place, technologies are of interest which would enable large-scale, on-site manufacturing of concrete structures in accordance with the demands of contemporary architectural and structural design. The article at hand evaluates the state-of-the-art with respect to these requirements and presents the CONPrint3D concept for on-site, monolithic 3D-printing as developed at the TU Dresden. This concept is driven by the demands and boundary conditions of construction practice. It complies with common architectural norms, valid design codes, existing concrete classes and typical economic constraints. Furthermore, it targets the use of existing construction machinery to the highest possible extent. The interdisciplinary team of authors illuminates various perspectives on the new technology: those of mechanical engineering, concrete technology, data management, and construction management. Some representative results of completed work in these fields are presented as well.

Patryk Jędrzejczak, M. Collins, T. Jesionowski et al.

É. Forcael, I. Ferrari, Alexander Opazo-Vega et al.

The construction industry is experiencing changes in its processes and work methods, and the advancement of new technologies in recent decades has led to a new concept known as Construction 4.0, coined in 2016 in Germany. Since its definition is still diffuse, it was deemed necessary to conduct a review on the publications in this field to grasp how this concept is being understood. For that purpose, a bibliometric analysis was conducted among 260 research articles using seven keywords. The results reveal that the number of publications is growing exponentially, with the USA, the UK, and China being leaders in this field; besides, four technologies are essential to understand Construction 4.0 at present time: 3D printing, big data, virtual reality, and Internet of Things. The results of this review suggest that further reviews should be conducted every 3 years to grasp the rapid evolution of Construction 4.0.

Md. Aslam Hossain, A. Zhumabekova, S. Paul et al.

Construction industry is very labor-intensive and one of the major sources of employment in the world. The industry is experiencing low productivity with minimum technological innovations for decades. In recent times, various automation technologies including 3D printing have received increasing interests in construction. 3D printing in construction is found to be very promising to automate the construction processes and have the potential of saving laborious work, material waste, construction time, risky operation for humans, etc. There has been a comprehensive body of research conducted to understand the recent advances, future prospects and challenges of large-scale adoption of 3D printing in construction projects. Being one the labor-intensive industries, this study also investigates the possible impact on the labor market with increasing adoption of 3D printing in construction. It is found that 3D printing can reduce significant number of labors which can solve the labor shortage problem, especially for the countries where construction is heavily dependent on immigrant workers. In contrast, 3D printing might not be favorable for the countries where construction is one of the main workforces and labor is less expensive. Moreover, 3D construction printing will also require people with special skills related to this new technology.

Rui Duan, Hui Deng, Mao Tian et al.

Computer vision-based deep learning object detection algorithms have been developed sufficiently powerful to support the ability to recognize various objects. Although there are currently general datasets for object detection, there is still a lack of large-scale, open-source dataset for the construction industry, which limits the developments of object detection algorithms as they tend to be data-hungry. Therefore, this paper develops a new large-scale image dataset specifically collected and annotated for the construction site, called Site Object Detection dAtaset (SODA), which contains 15 kinds of object classes categorized by workers, materials, machines, and layout. Firstly, more than 20,000 images were collected from multiple construction sites in different site conditions, weather conditions, and construction phases, which covered different angles and perspectives. After careful screening and processing, 19,846 images including 286,201 objects were then obtained and annotated with labels in accordance with predefined categories. Statistical analysis shows that the developed dataset is advantageous in terms of diversity and volume. Further evaluation with two widely-adopted object detection algorithms based on deep learning (YOLO v3/ YOLO v4) also illustrates the feasibility of the dataset for typical construction scenarios, achieving a maximum mAP of 81.47%. In this manner, this research contributes a large-scale image dataset for the development of deep learning-based object detection methods in the construction industry and sets up a performance benchmark for further evaluation of corresponding algorithms in this area.

Tianpei Tang, Zhaopeng Liu, Meining Yuan et al.

Unsafe behaviors among construction workers remain a leading cause of accidents in the construction industry. Previous studies have primarily relied on structural equation modeling and causal inference approaches to investigate the determinants of workers’ safety behavior. However, these methods are often limited in their ability to address confounding bias inherent in observational data and tend to focus on isolated effects of individual variables, thereby overlooking the complex interactions between organizational and individual factors. To overcome these limitations, this study applies the Categorical Boosting (CatBoost) algorithm to examine the joint organizational and individual mechanisms underlying construction workers’ safety behavior. CatBoost is particularly suitable for small- to medium-sized datasets and is capable of automatically capturing complex, nonlinear relationships among variables. Leveraging the SHAP interpretability framework, both main-effect and interaction analyses are conducted to systematically identify the most influential determinants. The results demonstrate that CatBoost outperforms eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models in predicting safety-related outcomes. Prosociality (PSO) is identified as the most influential predictor, followed by personal proactivity (PAC). Interaction analyses further reveal that organizational attributes—such as prosociality, loyalty, and mutual assistance—play a critical role in cultivating a safety-oriented organizational climate, while an optimistic personal attitude further enhances safety performance on construction sites. Overall, these findings provide meaningful theoretical insights and practical implications for improving safety management in the construction sector.

Wilma Johanna Schmidt, Diego Rincon-Yanez, Evgeny Kharlamov et al.

Numerous digitisation activities in manufacturing led to an enormous increase in available, accessible data. Knowledge graphs (KGs) become increasingly popular in this domain as they show strengths in integrating different data sources and serve as a basis for downstream tasks. Yet, constructing a KG is still a challenging and time-consuming process. Neuro-symbolic AI approaches, especially with powerful LLMs, have shown promising potential in research and industry and can support KG construction. Nevertheless, KG construction with neural methods must be aware of, or ideally even handle, the inexplicability of results when applying the KG to downstream manufacturing tasks, e.g., tasks of reliability- or safety-relevance. This makes it interesting to evaluate the utilisation of neuro-symbolic AI and LLMs in KG construction in manufacturing. To the best of our knowledge, there is no systematic literature review on neuro-symbolic AI and LLMs in KGs in manufacturing to date. Hence, this paper conducts a systematic literature review on neuro-symbolic AI and LLMs in KG construction in manufacturing. We show a solid increase of relevant publications on manufacturing KG construction and further show that BERT embeddings, RNN encodings, especially BiLSTM, CRF decodings, and, recently, LLMs, are common components of knowledge extraction from text documents to build KGs in manufacturing. With this systematic review, we support both further research and industry application in this field. The main question to guide this review is “Which role play neuro-symbolic AI, especially LLM approaches in knowledge graph construction for manufacturing?”.

Obinna C. Madubuike, C. Anumba, Rana Khallaf

The emergence of digital twin technology presents tremendous opportunities for several industry sectors. A digital twin is defined as the virtual representation of a physical asset that collects and sends real-time information. A digital twin collects data from the physical asset in real-time and uses this data to create a virtual model of the physical object. Its functionality depends on the bi-directional coordination of data between the physical and virtual models. This is likened to cyber-physical systems, which seek to provide bi-directional coordination between the physical and virtual worlds. While digital twins have found applications in the various industrial sectors such as aerospace, manufacturing, and industrial engineering, their applications in the construction industry are relatively limited. Although some level of progress has been made in the construction industry with the application of a digital twin, it still lags in other sectors. Virtual models of constructed facilities are developed and used to plan and construct the actual facility, with changes in the physical facility being automatically reflected in the virtual model based on real-time data and vice-versa. The digital twin shows promising possibilities in the design, construction, operation, and maintenance of a facility. This paper reviews the development and implementation of digital twin technology in the construction industry and compares its use with other industries while assessing the benefits of DT to the construction industry. A systematic literature review including a thematic analysis was employed to address the purpose of this study. Limitations associated with the existing and emerging applications are also identified. It concludes by highlighting the importance of DT applications in the construction sector.

Wasantha Rajapakshe

The construction industry has a significantly contribute to the economy of Sri Lanka. However, in recent years, its overall share of the national output has declined, primarily due to the impacts of the COVID-19 pandemic, political instability, and ongoing economic challenges. Many construction firms halted projects and laid off employees, highlighting the critical need for effective risk management during crises to predict and mitigate risks. This study explores emerging risk factors in Sri Lanka's construction sector post-crisis. Using a three-phase linear decision-making model, the research combines a literature review, a survey of 290 construction professionals, and structural equation modelling (SEM) to identify major risk factors across four phases of risk management. Key risks out of 23 include general factors like health and safety issues, material costs, regulations, political interference, corruption, and labor shortages, while phase-specific risks involve delays, budget overruns, payment delays, and cancellations. The study revises the risk registers to improve risk management strategies. While the findings are context-specific to Sri Lanka, they may offer indicative insights for other developing countries facing similar crisis-driven disruptions. The practical implications extend to multinational and local companies, supported by data from 22 countries, offering a comprehensive framework for addressing construction industry challenges in volatile environments. The novelty of this study lies in its use of theoretical triangulation to align Classical Risk Management theory with real-world operational risk factors, revealing critical overlaps, behavioral influences, and contextual gaps in traditional frameworks.

PENG Xuefeng1 , JI Enyue1, 2 , CHEN Shengshui1, 2, FU Zhongzhi1, 2, ZHANG Yijiang1, 2

The deformation simulation method of core wall dams has always been a hot and difficult topic in the industry. Traditional grid-based methods, such as finite element method, finite volume method and finite difference method, are often applied to deformation analysis. However, when dealing with large deformation problems, the Jacobian matrix becomes abnormal due to mesh distortion, making the calculation impossible. Therefore, within the basic framework of the material point method, this paper constructs the convective particle Gaussian interpolation function and combines it with the particle interpolation function to propose the adaptive interpolation material point method (AIMPM) applicable to fluid-solid coupling problems. Taking the landslide of the Carsington core wall dam as an example, the AIMPM is used to analyze the evolution law of the whole process from the construction period to the instability and landslide of the dam. The results show that: (1) According to the actual construction situation of the dam, the AIMPM can accurately describe the development process of deformation and pore pressure during the construction stage of the dam body; (2) Under the condition of obtaining the initial stress during the construction of the dam body, the Adaptive Interpolation Material Point Method (AIMPM) can simulate the complete evolutionary process of the dam from the formation of the initial sliding surface to the final accumulation body after the dam break; (3) By capturing the running characteristics of particles, the failure process of the dam body can be divided into three stages: rigid body displacement stage, stress equilibrium stage and velocity convergence stage. Through the refined simulation of the classic core wall dam, it is indicated that the adaptive interpolation material point method proposed in this paper can be flexibly applied in both small strain and large deformation fields, providing an effective approach for the deformation and failure analysis of core wall dams.

Jui-Sheng Chou, Nguyen-Ngan-Hanh Pham

Abstract Effective risk management is crucial in the construction industry, which has a substantial economic impact but is vulnerable to high financial risks due to volatile material costs and complex project-based financial structures. This study presents a new hybrid model to improve the prediction of financial distress for Taiwanese-listed construction companies. The research compares four boosting-based ensemble learning models, advanced deep learning models, and improved ensemble models that incorporate a novel approach using the Multi-Criteria Decision-Making (MCDM) technique, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to enhance feature selection. Experimental results show that while TOPSIS-eXtreme Gradient Boosting (TOPSIS-XGBoost) is highly effective at managing imbalanced financial datasets, Light Gradient Boosting Machine (LightGBM) performs better in balanced environments. Both models exhibit substantial performance gains when integrated with the Forensic-Based Investigation (FBI) optimization algorithm, resulting in the optimized hybrids—FBI-TOPSIS-XGBoost and FBI-LightGBM—which achieve marked improvements in predictive accuracy. These optimized models consistently outperform benchmark approaches, including the Altman Z-score, Zmijewski X-score, Logistic Regression, and Random Forest, across multiple evaluation metrics. To enhance transparency and interpretability, a global SHapley Additive exPlanations (SHAP) analysis was conducted, revealing that profitability and per-share index indicators are the primary determinants driving model predictions. Additionally, an expert system interface has been developed to enhance the practical usability of these models. These findings strengthen the methodological foundation for predicting financial distress and provide stakeholders with valuable tools for mitigating risk in Taiwan’s construction industry.

Indranil Nandi, Alok Kumar, Fahad Imam et al.

The COVID-19 pandemic lockdown provided an unprecedented opportunity to examine changes in India’s air quality following abrupt reductions in anthropogenic emissions, particularly from transportation, industry, and construction. While many studies reported substantial pollution declines during the lockdown, most focused exclusively on this period, neglecting the subsequent ‘unlock’ phase, the influence of transboundary pollution, and the need to distinguish between emission-driven and meteorology-driven changes in PM _2.5 . Our study addresses these gaps by isolating the contributions of meteorological variability and activity restrictions on PM _2.5 across the entire lockdown and unlock phases (February 24-June 30, 2020) using a high-resolution modelling framework and satellite-derived PM _2.5 data. Through our WRF-Chem modeling study, we found that PM _2.5 concentrations decreased by 29% post-lockdown, compared to a 21% decline over the same period in preceding years, with satellite observations showing similar reductions of 31% and 22%, respectively. However, only an additional 8–9% reduction in 2020, beyond the typical interannual variability, was directly attributable to emission controls, while meteorological factors largely influenced the overall decline. The most pronounced PM _2.5 decline occurred in the Indo-Gangetic Plain during the unlock phase. Despite the initial improvements, restrictions on transportation, industry, and construction alone were insufficient to bring PM _2.5 levels below the National Ambient Air Quality Standards. A key finding is that persistent emissions from the residential sector, which remained largely unaffected during the lockdown, significantly limited the overall reduction in PM _2.5 . Without targeted interventions to address household emissions, such as promoting cleaner fuels and improving waste management to prevent garbage burning, India will struggle to achieve sustained air quality improvements. Our results emphasize the urgent need for integrated, regionally tailored, long-term strategies that address all major pollution sources to ensure lasting reductions in PM _2.5 levels. Implementing comprehensive measures can significantly improve India’s air quality, ensuring a healthier and more sustainable environment.

SHAO Qiming, BIAN Yong, SHI Aimin

As one of the modern enterprise management systems, the Occupational Health and Safety Management System (OHSMS) has garnered increasing attention. The OHSMS has undergone continuous refinement and expansion across various fields, emerging as a pivotal indicator of enterprise competitiveness. Currently, both the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International and the China National Accreditation Service for Conformity Assessment (CNAS) require for establishing occupational health management related to laboratory animal work. However, within the domestic laboratory animal industry, development of OHSMS is relatively lagging behind due to unfamiliarity with laws and regulations and a lack of experienced management personnel. Consequently, the OHSMS in laboratory animal institutions is still in its early stage. Drawing on the authors' practical experience in establishing OHSMS in laboratory animal institutions, this article first outlines domestic and international occupational health laws, regulations, and safety management systems for laboratory animals, as well as common occupational diseases and their associated risk factors in China. Subsequently, this article highlights key elements for the construction of OHSMS in laboratory animal institutions in areas such as establishing occupational health and safety regulations, conducting training, performing occupational health examinations for staff, monitoring on-site occupational hazard factors, implementing the "three simultaneous" system for occupational disease prevention facilities in construction projects, creating and maintaining occupational health records, developing a notification management system for occupational hazards, and formulating emergency response plans for occupational disease hazard accidents. These points are intended as a reference for professionals in the industry.

Lucica Iconaru, Carmen Gasparotti

The design of ships has changed dramatically since the 1970s. We have shifted from manual drafting to digital tools and computers, mostly because computer technology has greatly improved. Nowadays, with the growth of smart digitalization in Industry 4.0, using modern digital software and tools makes ship design more efficient and enhances its quality throughout a ship's entire lifespan. However, this shift has also made operations more complex and requires users of the software to have more specialized training. Today, technologies like automated optimization, simulation-based design, managing the entire product lifecycle, digital twins, and artificial intelligence are commonly used in the shipping industry. These technologies are applied during both the design and construction phases, as well as in preparing and inspecting ships. This paper reviews major advances in these areas and discusses how the industry can address current and future challenges.

F. Khosrowshahi, Yusuf Arayici

Purpose – Building information modelling (BIM) implementation is a major change management task, involving diversity of risk areas. The identification of the challenges and barriers is therefore an imperative precondition of this change process. This paper aims to diagnose UK's construction industry to develop a clear understanding about BIM adoption and to form an imperative step of consolidating collective movements towards wider BIM implementation and to provide strategies and recommendations for the UK construction industry for BIM implementation.Design/methodology/approach – Through comprehensive literature review, the paper initially establishes BIM maturity concept, which paves the way for the analysis via qualitative and quantitative methods: interviews are carried out with high profile organisations in Finland to gauge the best practice before combining the results with the analysis of survey questionnaire amongst the major contractors in the UK.Findings – The results are established in the form ...

O. Samuelson, L. Stehn

Digital transformation (DT) is expected to contribute to the construction industry's ability to meet climate and sustainable challenges and increase companies' productivity. This study aims to explore requirements for, and factors affecting DT in the construction industry. This research goes beyond the technology perspective and focus on factors needed to transform the potential of digitalisation to benefits for organisations in the construction industry. A structured literature review is performed where knowledge gaps are identified, and a framework is developed that maps the required changes, as well as the associated challenges, constraints, and implications. The construction industry´s business-to-business logic, and the fragmented and project-based structure is found to have impact on the industry´s development within DT. Mainly regarding the DT aspects disruption, structural changes, organisational barriers, and the central aspect value creation. The understanding of DT by scholars and practitioners in the construction industry is found immature and this calls for further research. The research contributes to understanding of the concept DT and proposes, based on earlier DT literature, an adjusted framework for DT in construction, and points out key areas where research in construction has gaps to fill.