Hasil untuk "Construction industry"

D. Camacho, P. Clayton, William J. O'brien et al.

Jiangwei Luo, Mohd Wira Mohd Shafiei, Radzi Ismail

This study addresses the overlooked dynamic interaction between strategic agility and absorptive capacity in turbulent environments, particularly within project-based enterprises where knowledge flows are fragmented. By constructing an integrative framework, it examines how strategic agility influences enterprise performance via absorptive capacity under varying environmental turbulence levels. Using data from 198 Chinese construction industry practitioners and PLS-SEM analysis, results reveal that strategic agility dominates enterprise responses, with absorptive capacity mediating this effect. Notably, in high turbulence, the mediation path buffers the negative impact. The study advances organizational adaptability theory and offers guidance for capability development in turbulent contexts.

David Tschirschwitz, Volker Rodehorst

Reproducibility and replicability are critical pillars of empirical research, particularly in machine learning, where they depend not only on the availability of models, but also on the datasets used to train and evaluate those models. In this paper, we introduce the Construction Industry Steel Ordering List (CISOL) dataset, which was developed with a focus on transparency to ensure reproducibility, replicability, and extensibility. CISOL provides a valuable new research resource and highlights the importance of having diverse datasets, even in niche application domains such as table extraction in civil engineering. CISOL is unique in that it contains real-world civil engineering documents from industry, making it a distinctive contribution to the field. The dataset contains more than 120,000 annotated instances in over 800 document images, positioning it as a medium-sized dataset that provides a robust foundation for Table Structure Recognition (TSR) and Table Detection (TD) tasks. Benchmarking results show that CISOL achieves 67.22 mAP@0.5:0.95:0.05 using the YOLOv8 model, outperforming the TSR-specific TATR model. This highlights the effectiveness of CISOL as a benchmark for advancing TSR, especially in specialized domains.

Harm Derksen, Benjamin Lovitz

We present explicit quantum channels with strictly sub-additive minimum output Rényi entropy for all $p>1$, improving upon prior constructions which handled $p>2$. Our example is provided by explicit constructions of linear subspaces with high geometric measure of entanglement. This construction applies in both the bipartite and multipartite settings. As further applications, we use our construction to find entanglement witnesses with many highly negative eigenvalues, and to construct entangled mixed states that remain entangled after perturbation.

Mani Amani, Reza Akhavian

Construction robotics increasingly relies on natural language processing for task execution, creating a need for robust methods to interpret commands in complex, dynamic environments. While existing research primarily focuses on what tasks robots should perform, less attention has been paid to how these tasks should be executed safely and efficiently. This paper presents a novel probabilistic framework that uses sentiment analysis from natural language commands to dynamically adjust robot navigation policies in construction environments. The framework leverages Building Information Modeling (BIM) data and natural language prompts to create adaptive navigation strategies that account for varying levels of environmental risk and uncertainty. We introduce an object-aware path planning approach that combines exponential potential fields with a grid-based representation of the environment, where the potential fields are dynamically adjusted based on the semantic analysis of user prompts. The framework employs Bayesian inference to consolidate multiple information sources: the static data from BIM, the semantic content of natural language commands, and the implied safety constraints from user prompts. We demonstrate our approach through experiments comparing three scenarios: baseline shortest-path planning, safety-oriented navigation, and risk-aware routing. Results show that our method successfully adapts path planning based on natural language sentiment, achieving a 50\% improvement in minimum distance to obstacles when safety is prioritized, while maintaining reasonable path lengths. Scenarios with contrasting prompts, such as "dangerous" and "safe", demonstrate the framework's ability to modify paths. This approach provides a flexible foundation for integrating human knowledge and safety considerations into construction robot navigation.

Najeeb Manhanpally, Praveen Nagarajan, Suman Saha et al.

Abstract In search of sustainable construction material as an alternative to existing ordinary cement concrete, the use of recycled aggregates in geopolymer concrete has garnered significant attention. Using industrial by-products such as dolomite and ground granulated blast furnace slag (GGBS), combined with alkali activators like sodium silicate and sodium hydroxide to prepare geopolymer concrete, offers a promising substitute to traditional Portland cement concrete. This study explores the potential of incorporating recycled aggregates, specifically focusing on the benefits of treated recycled-aggregates (TRCA) into the concrete for improving sustainability of geopolymer concrete. Mechanical grinding treatment is found to be effective in removing adhered mortar from recycled-aggregates, and thus improving aggregate quality and reducing porosity and micro-cracks. By systematically analyzing the effects of untreated and treated recycled aggregates on concrete properties, this research provides a comprehensive understanding of how treatment processes can mitigate the limitations of RCA and optimize the material’s performance. Incorporating treated recycled aggregates enables the construction industry to adopt more sustainable building practices, thus helping global efforts to minimise damages to the environment. The experimental results demonstrated that treating recycled aggregates showed key engineering properties of geopolymer concrete similar to normal geopolymer concrete with reduction less than 5% at 100% replacement level. Geopolymer concrete, made from GGBS and dolomite with treated recycled aggregates, provides a sustainable substitute for conventional concrete, offering environmental and structural advantages. This paper promotes the use of treated recycled aggregates to decrease the dependency on natural resources, therefore promoting the circular economy within the building industry. The findings of this study are expected to advance the knowledge of recycled aggregate utilization in geopolymer concrete, offering practical insights for construction professionals and researchers. The research seeks to develop novel, sustainable, and high-performance construction materials that meet environmental and structural requirements.

Tchedele Langollo Yannick, Bilkissou Alim, Mohamadou Alpha Ali et al.

Abstract This study investigates the potential of micro glass powder as a supplementary cementitious material in Belite cement matrices, addressing both environmental concerns and performance optimization. With the cement industry contributing significantly to global CO2 emissions and glass waste posing recycling challenges, this work explores the synergistic recycling of soda-lime glass in cementitious systems. The primary objectives were to evaluate the influence of glass powder on hydration kinetics, mechanical properties, and microstructure, while developing predictive models for strength behavior. Results demonstrate that glass powder addition (5–35% by weight) enhances workability and extends setting times, with optimal mechanical performance observed at 5–20% substitution. The pozzolanic reactivity of glass powder improved compressive strength by up to 23.6% (56 days) and flexural strength by 18.4% (300 days) compared to plain cement mortars. Microstructural analyses confirmed the formation of secondary calcium silicate hydrates (C-S-H) and reduced porosity in modified pastes. Statistical modeling yielded high-accuracy predictive equations (R2 ≥ 0.92) for strength properties as functions of composition, curing time, and microstructure. These findings highlight the dual benefit of glass powder in mitigating waste and enhancing cement performance, supporting its viability as a sustainable construction material.

Yinxiang Zhou, Weili Liu, Xuying Lv et al.

Abstract Global warming, resulted from greenhouse gases emission, has significant impacts on natural ecosystem and economic development. Construction is a leading industry in China's economy with high carbon emissions. Improving the carbon emission efficiency and investigating the driving factors of carbon emission efficiency in China's building industry are very important for the sustainable development of construction industry and the goal of China's carbon emission reduction. This paper measures the total-factor carbon emission efficiency of construction industry from 2003 to 2016 by applying Super-SBM DEA approach. Then the interior driving factors and cross-industrial shock effects of carbon emission efficiency are investigated by industry GVAR model. Empirical results show that the carbon emission efficiency in China's construction industry is low with a downward trend. With regard to interior driving factors, technological progress and energy structure adjustment can promote the carbon emission efficiency of construction industry, but the economic scale with extensive development mode exerts a certain negative influence. From the perspective of cross-industrial driving factors, economy scale, energy structure and technological progress play an important role in the carbon emission efficiency in manufacturing and electricity/gas/water industry, while relatively faint in other industries. Inter-industrial economy scale and energy structure except for transportation industry exert negative effects on carbon emission efficiency, whereas the impacts of exterior technological progress of other industries except for the mining industry are positive. In order to promote industrial carbon emission efficiency, the carbon reduction mechanism for the common but differentiated responsibilities among industries should be established.

M. Loosemore, N. Malouf

Abstract Poor safety is a perennial problem for the construction industry worldwide. While there has been a large amount of research on construction safety training and its importance in developing positive safety attitudes, much of the evidence has been anecdotal. To address this gap in knowledge, this paper presents the results of an attitudinal survey of 228 construction employees from a variety of professional and trade backgrounds operatives in Australia who went through mandatory site safety training. It was found that the training was largely ineffective in changing workers’ safety attitudes. The minor change in safety attitudes that did occur were largely cognitive and behavioural in nature while the affective component of safety attitudes remained virtually unchanged. In other words, construction operatives emerged from the training with a slightly better knowledge of safety risks, a better intention to behave safely but not caring any more about safety as an issue. It was also found that gender, age and education are potential mediators in the safety attitude formation process. It is recommended that when developing safety training programs in the future, more attention should be paid to tailoring programs to the demographic characteristics of the people being trained and to the use of new interactive and immersive technologies and learner-centric andragogical pedagogies.

Raihan Maskuriy, A. Selamat, K. Ali et al.

Technology and innovations have fueled the evolution of Industry 4.0, the fourth industrial revolution. Industry 4.0 encourages growth and development through its efficiency capacity, as documented in the literature. The growth of the construction industry is a subset of the universal set of the gross domestic product value; thus, Industry 4.0 has a spillover effect on the engineering and construction industry. In this study, we aimed to map the state of Industry 4.0 in the construction industry, to identify its key areas, and evaluate and interpret the available evidence. We focused our literature search on Web of Science and Scopus between January 2015 and May 2019. The search was dependent on the following keywords: “Industry 4.0” OR “Industrial revolution 4.0” AND TOPIC: “construction” OR “building”. From the 82 papers found, 20 full-length papers were included in this review. Results from the targeted papers were split into three clusters: technology, security, and management. With building information modelling (BIM) as the core in the cyber-physical system, the cyber-planning-physical system is able to accommodate BIM functionalities to improve construction lifecycle. This collaboration and autonomous synchronization system are able to automate the design and construction processes, and improve the ability of handling substantial amounts of heterogeneity-laden data. Industry 4.0 is expected to augment both the quality and productivity of construction and attract domestic and foreign investors.

Bo Li, Shuangping Han, Yafei Wang et al.

The carbon emissions from the construction industry have a significant impact on China's ability to successfully achieve its 2030 carbon peak target. The paper reports the feasibility of carbon peaks in China's construction industry based on two perspectives of factor decomposition and peak prediction. First, the Generalized Dividing Index Method factorizes the carbon emissions of China's construction industry from 2001 to 2017, and quantifies the contribution rate of each influent factor. Second, a baseline scenario, a low-carbon energy-saving scenario, and a technology breakthrough scenario are constructed. The carbon peaks of the China's construction industry in the three scenarios are then predicted for 2018-2045. The results are as follows: Firstly, GDP has the highest cumulative contribution rate to China's construction industry carbon emissions, and labor productivity and the output carbon intensity have a depressing effect on carbon emissions in that industry. The contribution rate of energy consumption to carbon emissions is always positive and grows year by year, whereas the energy intensity and carbon intensity of energy consumption have great potential for reducing carbon emissions in the future. The number of laborers and the per capita carbon emissions of the construction industry, the total labor force in each industry, and the proportion of the labor force in the construction industry have contributed to the carbon emissions of the construction industry. Second, under the baseline scenario, China's construction industry achieves carbon peaks in 2045, with a peak of 50,935,390 tons. Under the low-carbon energy-saving scenario, the carbon peak of the construction industry occurs in 2030, with a peak value of 31,685,580 tons. Under the technological breakthrough scenario, the carbon peak time of the construction industry is the earliest (2020), and the peak value is the lowest (29,008,400 tons). This study has important implications for the carbon peaks at the national macro level.

Seyed Amin Tabatabaei, Sarah Fancher, Michael Parsons et al.

We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated. Traditional machine learning approaches, which require costly retraining with each taxonomy update, become impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. However, applying them to large and dynamic taxonomies presents unique challenges as the vast number of labels can exceed LLMs' input limits. In this paper, we present novel methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. Our approach avoids retraining by leveraging zero-shot HMC for real-time label assignment. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines, and demonstrate significant improvements in both classification accuracy and cost-efficiency. By developing a tailored evaluation framework for dynamic taxonomies and publicly releasing our code, this research provides critical insights into applying LLMs for document classification, where the number of classes corresponds to the number of nodes in a large taxonomy, at an industrial scale.

Xinyi Zheng, Chen Wei, Shenao Wang et al.

The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static analysis methods struggle with high false positive rates and are easily thwarted by obfuscation and dynamic code execution techniques. While dynamic analysis approaches offer improvements, they often suffer from capturing non-package behaviors and employing simplistic testing strategies that fail to trigger sophisticated malicious behaviors. To address these challenges, we present OSCAR, a robust dynamic code poisoning detection pipeline for NPM and PyPI ecosystems. OSCAR fully executes packages in a sandbox environment, employs fuzz testing on exported functions and classes, and implements aspect-based behavior monitoring with tailored API hook points. We evaluate OSCAR against six existing tools using a comprehensive benchmark dataset of real-world malicious and benign packages. OSCAR achieves an F1 score of 0.95 in NPM and 0.91 in PyPI, confirming that OSCAR is as effective as the current state-of-the-art technologies. Furthermore, for benign packages exhibiting characteristics typical of malicious packages, OSCAR reduces the false positive rate by an average of 32.06% in NPM (from 34.63% to 2.57%) and 39.87% in PyPI (from 41.10% to 1.23%), compared to other tools, significantly reducing the workload of manual reviews in real-world deployments. In cooperation with Ant Group, a leading financial technology company, we have deployed OSCAR on its NPM and PyPI mirrors since January 2023, identifying 10,404 malicious NPM packages and 1,235 malicious PyPI packages over 18 months. This work not only bridges the gap between academic research and industrial application in code poisoning detection but also provides a robust and practical solution that has been thoroughly tested in a real-world industrial setting.

Dennis Junger, Max Westing, Christopher P. Freitag et al.

Progressing digitalization and increasing demand and use of software cause rises in energy- and resource consumption from information and communication technologies (ICT). This raises the issue of sustainability in ICT, which increasingly includes the sustainability of the software products themselves and the art of creating sustainable software. To this end, we conducted an analysis to gather and present existing literature on three research questions relating to the production of ecologically sustainable software ("Green Coding") and to provide orientation for stakeholders approaching the subject. We compile the approaches to Green Coding and Green Software Engineering (GSE) that have been published since 2010. Furthermore, we considered ways to integrate the findings into existing industrial processes and higher education curricula to influence future development in an environmentally friendly way.

Viktor I. Karagodin

When planning the operation of land transport and transport technology facilities, their technical condition is not sufficiently taken into account. This can lead to unplanned failure of the machines and failure of the planned work by the remaining machines. The proposed methods of distributing machines by objects and types of work are based on the theory of aging of machines, but unlike the performance potential of machines, which reflects the technical condition of an average machine, they are focused on a specific machine, the probability of failure of which is determined using technical diagnostic methods. The results of the study of the dependence of the probability of failure of the car on the value of the inter-control period, the patterns of change in the probability of failure during the inter-control period and with an increase in the mileage of the car are presented. The goal is to increase the efficiency of the use of ground transportation and transportation technology facilities. Method and methodology. The theory of aging of machines, mathematical modeling, statistical methods of analysis. Results. New dependences of the probability of a car failure on the value of the inter-control period, the regularity of the change in the probability of failure during the inter-control period and with an increase in the mileage of the car are obtained and justified. The field of application of the results is the operation of ground transportation and transportation technology facilities.

Lin ZHANG, Guofa WANG, Zhiguo LIU et al.

The current situation and future development trend of the intelligent construction market in coal mines are the focus of attention for industry management departments, coal production enterprises, technology support enterprises, and other potential enterprises. It is also an important guiding direction for capital investment, technology research and development, and talent cultivation in the industry. Construct a six dimensional Porter diamond model based on production factors, demand conditions, supporting industries, enterprise strategies, industry opportunities, and government support policies to macroscopically analyze the competitive advantages of the coal mine intelligent construction market; Based on historical data such as the number of coal mines, production capacity, fixed assets investment in mining and dressing industry, total investment in intelligent construction, and construction results from 2013 to 2023, select data in different dimensions such as quantity indicators, production capacity indicators, and investment indicators, calculate the market penetration rate of intelligent construction of coal mines and perform curve fitting, and further modify the fitted curve according to the concentration of coal enterprise scale, concentration of coal production capacity distribution, and concentration of coal enterprise attributes; To further measure the market vitality of coal mine intelligent construction, taking the first batch of 71 intelligent demonstration coal mines in China as samples, the market capacity of coal mine intelligent construction from 2025 to 2035 is predicted. In the case where it is difficult to obtain comprehensive and accurate market data of all subsystems of coal mine intelligent construction over the years, SAC, SAM, and SAP product market statistical data are used to verify the reliability of coal mine intelligent market analysis and prediction results; Based on policy guidance, technological status, and actual construction, five stages of the development of the coal mine intelligent construction market have been proposed: budding, cultivating pilot projects, demonstration construction, comprehensive construction, and advanced intelligence. Based on the Jeffrey Moore divide theory, the characteristics of each stage of intelligent construction and the characteristics of early and mainstream markets have been analyzed, and the market gap and its factors in coal mine intelligent construction have been explored. Specific measures to bridge the gap have been proposed. China’s coal mine intelligent construction market has strong and sustained competitiveness; The market penetration rate is expected to reach 10% by 2026, with a market penetration rate of approximately 4.74% in 2023. This is a golden period for social capital investment, and lower investment can be used to leverage higher market share in the future; The market capacity for intelligent construction of coal mines is expected to exceed 320 billion yuan by 2025 and reach trillions of yuan by 2035; The current intelligent construction of coal mines is in a gap area between the early market and the mainstream market, and it is expected to cross the gap in 2−3 years.

Sunwook Kim, Albert Moore, Divya Srinivasan et al.

OCCUPATIONAL APPLICATIONS Work-related musculoskeletal injuries and disorders remain an important problem in the construction industry. Exoskeletons are an emerging wearable technology that assists or augments a user’s physical activity or capacity. This technology is a potential solution to reduce the physical demands and fatigue experienced by construction workers and help improve worker safety, health, and performance. As a first step towards enabling exoskeleton use in construction, we captured the perspectives of construction industry stakeholders regarding adopting exoskeletons and continued use in practice. Stakeholder responses highlighted several important questions and concerns, which were grouped using qualitative content analysis into three categories: (1) expected benefits, (2) exoskeleton technology adoption factors, and (3) perceived barriers to use. Uncertainties were expressed about the practical value and usability of exoskeleton technologies, and the impact of this technology on worker safety. Given this, and the limited state of current evidence, we summarize important research gaps that need to be addressed in future for successful adoption and use of exoskeleton technologies in the construction industry.

Q. Du, Jie Zhou, Ting Pan et al.

Abstract Coordinating the dilemma of economic development and reducing carbon emissions is of great significance to reaching China's energy-saving and emission-reduction targets. This paper investigates the decoupling relationship between economic growth and carbon emissions from the construction industry of China's 30 provinces, and uses the standard deviational ellipse method to explore the spatial evolution of carbon emissions and the economy. The results indicate that the economic development levels of most provinces were positively correlated with carbon emissions. The spatial differences in the decoupling state of provincial construction industry are significant, and the decoupling states of the same type exhibited a certain spatial aggregation phenomenon. Furthermore, the spatial distribution of the output value and carbon emissions exhibited a northeast-southwest pattern. The weighted mean centers of both were located in the east and moved towards the northwestern region. These results may provide a basis for assessing regional construction carbon emissions and formulating strategies for the coordinated development of low carbon emissions in the construction industry.

Raihan Maskuriy, A. Selamat, P. Marešová et al.

Technology and innovations have fueled the evolution of the fourth industrial revolution (Industry 4.0). Industry 4.0 spurs growth and development through its efficiency capacity, as documented in the literature. The growth of the construction industry is a subset of the universal set of the value of gross domestic product, and thus, industry 4.0 has a spillover effect on the engineering and construction industry. The aim of this paper is to map the state of Industry 4.0 in the construction industry from the point of view of manarial activities, such as investment management, project preparation, and an overall approach to the management of related activities. This study employed scoping review techniques to dissect the status quo for Industry 4.0 and the construction industry. The empirical results from the systematic and scoping review methods for the ten sampled publications revealed that information and communication technology (ICT)—Industry 4.0—has a significant positive impact on the growth of the construction industry. Therefore, construction practitioners should partner more with researchers in the ICT industry to enhance the automation of work processes and managerial activities in the engineering and construction industry.

Peng Wu, Yongze Song, Jianbo Zhu et al.

Abstract China has been the largest contributor to global carbon emissions since 2008, with its building and construction industry considered as one of the most significant sources. However, few studies have been conducted on analyzing the influencing factors of the carbon emissions following a life cycle perspective. This study aims to evaluate the carbon emissions of this industry from a life cycle perspective, including the extraction, manufacturing, construction and construction-related transportation, and building operation, using Logarithmic Mean Divisia Index. The key findings are: 1) the extraction and manufacturing of raw materials, and building operation are the two biggest contributors to the life cycle carbon emissions of the building and construction industry, accounting for 58% and 40% respectively; 2) the most effective strategies to reduce carbon emissions in the construction stage from 2000 to 2015 are to improve energy efficiency and lower emission factor; and 3) the most effective strategies to reduce carbon emissions in the building operation stage from 2000 to 2015 are to increase development density, improve emission factor, energy structure and industry structure. This study provides useful scientific evidence for policy makers to establish appropriate emission targets and relevant reduction strategies that are relevant to China's building and construction industry.