Hasil untuk "Construction industry"

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Wei Luo, Haiming Yao, Wenyong Yu

Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the \textbf{T}emplate-based \textbf{F}eature \textbf{A}ggregation \textbf{Net}work (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Additionally, a random masking strategy for input features is employed to enhance the overall inspection performance of the model. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry. Code is available at https://github.com/luow23/TFA-Net.

Yana Rodgers, Xiangmin Liu, Jingang Yi et al.

The share of construction trade jobs held by women and people with disabilities has remained stubbornly low in the face of chronic shortages of skilled labor. This study explores the potential of wearable assistive technologies to reduce these disparities. We use U.S. worker-level data to estimate employment and wage differences by gender and by mobility/strength impairments in construction and non-construction jobs. We also use occupational-level data to examine variations in workforce composition, physical skill requirements, and earnings across detailed construction occupations. Regression estimates indicate that being a woman and having strength and mobility impairments are associated with substantial employment and pay gaps in construction compared to non-construction jobs. Further analysis shows a high negative correlation between the representation of women and the ability levels required in those occupations. Finally, we discuss several wearable exoskeletons under development for people with upper-body and lower-body impairments, focusing on how these innovations could be integrated into construction jobs. These findings suggest that wearable exoskeletons that enhance manual dexterity, balance, and strength may improve the representation of women and people with disabilities in some of the higher-paying occupations in construction.

Haobo Shu, Junnan Cui, Yuhan Liu et al.

The application of antifouling paints to the surfaces of marine installations is the most economically efficient means for mitigating damage caused by marine biofouling in the shipping industry. However, conventional antifouling paints currently in widespread use can no longer meet the requirements of green antifouling. Although hydrogel coatings have made great progress in marine antifouling applications, current hydrogel coatings still suffer from construction difficulties and poor mechanical stability under wet conditions. In this paper, we innovatively exploit the phenomenon of the absorption of pyrogallol (PG) by large-molecular-weight polyvinylpyrrolidone (PVP), resulting in hydrophilic copolymer macromolecules, to propose a prepolymer-reactor rapid contact molding of sprayable hydrogel coatings. The PG/PVP copolymer produced microscopic reticular mimetic mussel adhesion protein (MAP) bioscaffolds via the chemical crosslinking of polyethyleneimine (PEI), contributed to the conversion of PG to PG-quinone upon the introduction of vanadium pentoxide particles, increased the hydrophobicity of the system and enhanced waterproof adhesion. The wet adhesion of the hydrogel coatings was measured up to 3.42 MPa via the micrometer scratch method, indicating that the prepared hydrogel coating had a stable adhesive force in a wet environment. The hydrogel coating was instantly molded on the surface of 304 stainless steel (SS) via two-step spraying. The swelling, friction, antifouling, and anticorrosion properties of the coatings were investigated along with the wet adhesion strength on the SS surfaces. The results showed that the hydrogel, after double cross-linking of PEI and V2O5, had a swelling rate within 30% and a low modulus along with stable lubricating properties. After the formation of the hydrogel coating, the inhibition rate of common bacteria and algae in the ocean reached more than 99%, and the electrochemical corrosion protection rate of SS reached 63.49%. This study provided ideas for improving the wet adhesion of hydrophilic marine antifouling coatings.

Sanghwan Cho, Namkon Lee, Min Ook Kim

The cement and concrete industry (CCI) are a dominant source of CO₂ emissions, contributing nearly 8 %. While traditional mitigation strategies focus on reducing emissions, CO₂ utilization presents a paradigm shift transforming CO₂ from an industrial by-product into a functional material component. This review provides a novel integrative perspective, evaluating carbonation curing, CO₂-reactive aggregates, and alternative binders in terms of physicochemical mechanisms, structural performance, and large-scale implementation potential. Carbonation curing has demonstrated the ability to accelerate hydration kinetics, enhance early-age strength, and refine pore structures while permanently sequestering CO₂. CO₂-modified aggregates, such as carbonated recycled concrete aggregates (RCA) and steel slag, not only reduce waste but also improve mechanical integrity. Emerging binders, including alkali-activated and magnesium-based cements, enable in-situ CO₂ mineralization, presenting a viable low-carbon alternative to Portland cement. These advancements contribute to increased durability, enhanced sulfate resistance, and superior microstructural stability. However, scaling up laboratory innovations to an industrial level remains a challenging task. High CO₂ capture costs, suboptimal carbonation kinetics, and the absence of regulatory frameworks hinder widespread implementation. This review highlights key knowledge gaps and suggests future research directions, including machine learning-driven mix optimization, automated carbonation control, and integrated life-cycle assessment. By shifting CO₂ from an environmental burden to a value-added resource, this study lays the foundation for next-generation carbon-neutral cementitious materials, advancing both sustainability and high-performance construction.

Ademilade Olubambi, Opeoluwa Akinradewo, Clinton Aigbavboa et al.

In South Africa, inefficient resource utilization in waste management results in a preference for disposal and landfilling as the lowest tier within the waste management hierarchy. Through a methodical approach to waste management system performance evaluation, using sustainability indicators, this study assists the construction industry to precisely define the current state of its waste management practice. This study conducted a comprehensive literature analysis to choose metrics that meet sustainability standards. To illustrate sustainability considerations across all lifetime dimensions, a table with twenty-two indicators was created. To enable sustainable measurement utilizing the triple-line dimension, a model-material flow system with a life-cycle mapping was modified. Exploratory factor analysis (EFA) was used to extract data. At each phase of the building lifespan, the sustainability performance measurement was carried out and validated. The findings indicate that sustainability was quantified at 0.5150 during the planning and design phase, with 0.4125 interpreted as below-average performance score during the initiation and feasibility testing phase, and with 0.500 during procurement, 0.5137 during construction and execution phases, 0.5250 during performance monitoring, 0.5350 during post-construction, and 0.5050 during renovation all having an average performance score. The waste management systems’ overall cumulative sustainability performance was determined to be 0.5009. The overall performance of the current waste management systems is satisfactory, but require improvement. Therefore, the government can use this sustainability appraisal to adopt a top-level policy for a sustainable waste industry in South Africa as part of its growing pursuit of sustainable development.

Pingyi Fan, Anbai Jiang, Shuwei Zhang et al.

With the rapid deployment of SCADA systems, how to effectively analyze industrial signals and detect abnormal states is an urgent need for the industry. Due to the significant heterogeneity of these signals, which we summarize as the M5 problem, previous works only focus on small sub-problems and employ specialized models, failing to utilize the synergies between modalities and the powerful scaling law. However, we argue that the M5 signals can be modeled in a unified manner due to the intrinsic similarity. As a result, we propose FISHER, a Foundation model for multi-modal Industrial Signal compreHEnsive Representation. To support arbitrary sampling rates, FISHER considers the increment of sampling rate as the concatenation of sub-band information. Specifically, FISHER takes the STFT sub-band as the modeling unit and adopts a teacher student SSL framework for pre-training. We also develop the RMIS benchmark, which evaluates the representations of M5 industrial signals on multiple health management tasks. Compared with top SSL models, FISHER showcases versatile and outstanding capabilities with a general performance gain up to 4.2%, along with much more efficient scaling curves. We also investigate the scaling law on downstream tasks and derive potential avenues for future work. Both FISHER and RMIS are now open-sourced.

Kartik Ganesh

This paper estimates the effect of Hurricane Harvey on wages and employment in the construction labor industry across impacted counties in Texas. Based on data from the Quarterly Census of Employment and Wages (QCEW) for the period 2016-2019, I adopted a difference-in-differences event study approach by comparing results in 41 FEMA-designated disaster counties with a set of unaffected southern control counties. I find that Hurricane Harvey had a large and long-lasting impact on labor market outcomes in the construction industry. More precisely, average log wages in treated counties rose by around 7.2 percent compared to control counties two quarters after the hurricane and remained high for the next two years. Employment effects were more gradual, showing a statistically significant increase only after six quarters, in line with the lagged nature of large-scale reconstruction activities. These results imply that natural disasters can generate persistent labor demand shocks to local construction markets, with policy implications for disaster recovery planning and workforce mobilization.

Qian Zhang, Chang Liu, Wenhui Zhu et al.

Given the challenges of innovation and adaptation to change, Construction 4.0 (C4.0) is triggering a revolution within construction and industry firms from automation to a greater level of digitalization. Despite the plethora of advantages and growing research interest in certain aspects of C4.0 technology implementation (C4.0TeIm), previous discourses have been largely fragmented and lack a comprehensive investigation of the factors influencing C4.0TeIm. To this end, this study aims to holistically investigate the influencing factors of C4.0TeIm and propose guidelines for future research directions. Informed by the United Nations twin green and digital transition perspectives, this study initiated its exploration in the background by delving into the potential intersections between C4.0 and sustainability. To achieve the aim, this study (i) reviewed 77 relevant articles and discerned a comprehensive list of factors influencing C4.0TeIm; (ii) outlined and quantified the influence and importance of the identified factors using social network analysis and validated results against the simplified analysis; and (iii) revealed gaps in the literature and proposed a research roadmap directing future research needs. The results show that 60 factors could collectively influence construction firms’ C4.0TeIm; they can be categorized into the external environment, technology competence, organizational factors, project-based factors, and technology challenges. The findings also reveal that further endeavors should emphasize those understudied factors such as “perceived overall organizational performance improvement”, “corporate strategy and management policy”, and “availability of resources”. Practically, the proposed research guidelines provide valuable references to accelerate C4.0TeIm in both academics and the business world and offer strategies for the top management of firms to maximize potential benefits and gain competitiveness.

Yi Xu, Shujie Liu, Felix Heisel

This study proposes an approach to combat construction waste in the architecture, construction, and engineering (ACE) industry by developing a disassemblable brick partition wall. Brick reuse is severely restricted by the presence of mortar; innovative approaches need to be explored. An existing strategy, utilizing mortarless interlocking, relies on non-standardized bricks. It is worth noting that these methods are not specifically created for disassembly, despite the fact that they theoretically could be. A relatively innovative technique for tightening and stabilizing brick units emerged in recent years, involving the utilization of metal components. Despite its potential, there are limited case studies of this approach. By drawing on two typical examples of pros and cons, MIFA 1862 and the UMAR Unit, we propose a new strategy and examine it from multiple perspectives. The findings of the analysis demonstrate how adaptable and versatile the proposed system is, allowing it to be modified into a variety of sizes and forms. Additionally, the system has proven to have considerable advantages in terms of construction speed, and energy efficiency throughout the structure's service time and in future use phases.

Javier Penuela, Cecile Ben, Stepan Boldyrev et al.

Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that are major electricity consumers is certainly a way forward. The indoor agriculture sector currently experiences a significant growth to partake in the sustainable production of high-quality food world-wide. As energy-related costs, up to 40\% of the total expenses, may preclude full maturity of this industry, DR participation can result in a win-win situation. Indeed, the agriculture system must transform and become a sustainable source of food for an increasing number of people worldwide under the constraints of preservation of soils and water, carbon footprint, and energy efficiency. We considered the case of the Russian Federation where indoor farming is burgeoning and already represents a load of several thousand megawatts. To show the viability of the indoor farming industry participation in implicit and explicit DR programs, we built a physical model of a vertical farm inside a phytotron with complete control of environmental parameters including ambient temperature, relative humidity, CO$_2$ concentration, and photosynthetic photon flux density. This phytotron was used as a model greenhouse. We grew different varieties of leafy plants under simulated DR conditions and control conditions on the same setup. Our results show that the indoor farming dedicated to greens can participate in DR without adversely affecting plant production and that this presents an economic advantage.

Hiroyasu Inoue, Yasuyuki Todo

This paper investigates how the granularity of supply-chain data affects the propagation of economic shocks through production networks. Using newly constructed establishment-level supply chains with product-level information links for Japan, we simulate disruption dynamics under alternative definitions of network nodes and input classifications. We show that defining inputs at the product level generates substantially larger propagation effects than industry-based classifications, indicating that coarse industry measures overstate input substitutability and underestimate systemic vulnerability. While establishment-level networks generally amplify shock propagation relative to firm-level networks, this effect is quantitatively modest, reflecting opposing forces of increased network complexity and greater substitution possibilities. We further demonstrate that incorporating establishment-level geographic information is critical for assessing region-specific shocks, as firm-level networks tend to overstate the impact of shocks originating in major metropolitan areas. Overall, our results highlight the importance of granular information on products, establishments, and geography for accurately evaluating supply-chain resilience and systemic risk.

Fengjie Wang, Chengming Liu, Lei Shi et al.

Previous industrial anomaly detection methods often struggle to handle the extensive diversity in training sets, particularly when they contain stylistically diverse and feature-rich samples, which we categorize as feature-rich anomaly detection datasets (FRADs). This challenge is evident in applications such as multi-view and multi-class scenarios. To address this challenge, we developed MiniMaxAD, a efficient autoencoder designed to efficiently compress and memorize extensive information from normal images. Our model employs a technique that enhances feature diversity, thereby increasing the effective capacity of the network. It also utilizes large kernel convolution to extract highly abstract patterns, which contribute to efficient and compact feature embedding. Moreover, we introduce an Adaptive Contraction Hard Mining Loss (ADCLoss), specifically tailored to FRADs. In our methodology, any dataset can be unified under the framework of feature-rich anomaly detection, in a way that the benefits far outweigh the drawbacks. Our approach has achieved state-of-the-art performance in multiple challenging benchmarks. Code is available at: \href{https://github.com/WangFengJiee/MiniMaxAD}{https://github.com/WangFengJiee/MiniMaxAD}

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

As part of the Internet of Things, industrial devices are now also connected to cloud services. However, the connection to the Internet increases the risks for Industrial Control Systems. Therefore, a threat analysis is essential for these devices. In this paper, we examine Industrial Internet of Things devices, identify and rank different sources of threats and describe common threats and vulnerabilities. Finally, we recommend a procedure to carry out a threat analysis on these devices.

Pavel V. Kalashnikov

Introduction. The paper provides a description of the scenario approach to risk control in the operation of complex technical systems under conditions of uncertainty. The scenario is understood as the optimal control action on the system parameters, which allows minimizing the possible costs associated with the implementation of the corresponding risk event. The aim of the study is to develop effective risk management methods for the operation of a complex dynamic system under conditions of uncertainty based on a scenario approach that makes it possible to implement the optimal control action on the parameters of an object in the event of a threat of an emergency. Materials and methods. The article describes the application of the scenario method to the management of risks that arise during the operation of a complex dynamic system under conditions of interval uncertainty. This approach is based on the application of methods of interval analysis and the theory of situational management. The scientific novelty of the implemented approach lies in the use of the interval analysis apparatus, which makes it possible to most correctly take into account the possible errors associated with measuring the values of the system parameters and apply the optimal control actions necessary for correction in the event that the permissible indicators go beyond the operability area. Discussion and conclusion. The mathematical model of risk control developed in the course of the study carried out during the process of functioning of a complex technical system under conditions of interval uncertainty based on the scenario approach allows the selection of optimal control actions (scenarios) that allow minimizing possible errors and inaccuracies that arise due to deviation from the calculated nominal values.

Hasan Burak Çavka

Shopping malls may be considered as controversial structures since they sometimes fail to comply with the expectations of the project stakeholders throughout the project life cycle. New mall projects often attract the attention of people since such a structure has a potential to reshape the neighborhood it is located in; however, the impact is usually negative. On the other hand, the parties involved in mall projects may be subject to criticism from both the public and the industry during the design, construction, and operation. In this study we conducted semi-structured interviews with five managers of an international company that provides real estate services worldwide, and mainly focuses on managing shopping centers within the context of Turkiye. During the interviews, we collected insights on shopping mall design and criteria that have an impact on the operational success or failure. We analyzed the interview data to understand the shopping mall design requirements from the experts’ perspectives. We summarized our investigation under three main categories as location, shop and brand mix, and design. Analyzed data indicates that the requirements and use of shopping malls evolve and change over time. The change is driven by things such as changing habits and expectations of the users and new marketing approaches. Understanding such changes is essential for designers and investors to propose new design approaches and space compositions in order to be able to adapt to the changes. Through our analysis of the collected data, we provided insights on requirements and new trends that affect the design of malls. As further explained in this paper, our analysis indicates a number of important topics during design such as the need to design to fit ever-changing spatial needs, providing feel-good environment for users, correct placement of spaces and stores related to each other, designing circulation that supports commercial activities, and designing with a consideration of operation and maintenance. According to the collected data, the trend of shopping mall design is towards integration of hybrid uses, free forms, more open spaces, increased emphasis on gastronomy, and enabling socializing while leveraging technology and being more sustainable.

Rujie Li, Peixuan Zhong, Yalong Zhang et al.

Difficult-to-machine materials such as stainless steel are widely used in the construction industry, because of their excellent mechanical properties and corrosion resistance. However, the poor tool-chip contact environment, severe tool wear, and heavy chip accumulation inhibit the machining efficiency. In this paper, 316L austenitic stainless steel was selected to investigate the effect of a variable-length restricted contact tool (VL-RCT), aiming at reducing the cutting temperature and increasing the tool life. A finite element simulation model of restricted contact cutting was established to investigate the machining parameters and restricted contact parameters on cutting performances and to clarify the mechanism of the VL-RCT in the cutting process. Additionally, cutting experiments were conducted to verify the cutting process mechanism. The results showed that the variable restricted contact structure efficiently reduced the cutting force and cutting temperature and improved the cutting performances of austenitic stainless steel. Both numerical simulation and cutting experiments reported that the trapezoidal restricted contact structure improved the cutting performance the best. Accordingly, this research provided theoretical guidance for the optimization of tool structure and the selection of cutting parameters, as well as a solid foundation for the future development of relevant design theories and methods for high-performance tools.

Lindomar Matias Gonçalves, Clara Mendoza-Martinez, Elém Patrícia Alves Rocha et al.

Steel is a crucial industrial product with applications in various sectors, such as construction, engineering, and industry. However, the steel industry generates significant waste, contributing to greenhouse gas emissions and environmental challenges. To address this issue, incorporating solid waste, especially sludge with high moisture content, into the steel industry’s operations is essential. This study aimed to construct and test an active indirect solar dryer for reducing the moisture content of sludge from a steel drawing industry. By employing principles of the circular economy and the environmental, social, and governance concept, the drying process showed promising results, achieving approximately 42% moisture reduction. This study involved collection and characterization of industrial sludge, design and assembly of a hybrid active indirect solar dryer, fluid dynamic analysis of the behavior of the air inside the device through CFD Ansys software 2012, tests with a thermographic camera to validate the simulation, and optimization of the sludge drying by calculating the thermal efficiency and drying efficiency of the equipment. The adoption of such drying processes can lead to substantial cost reductions in the transportation, handling, and landfilling of steel-drawing sludge, promoting innovation and aiding global steel industries in achieving their solid waste disposal targets.

Zhiwei Xu, Min Zhou, Xibin Zhao et al.

The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representations for code-related tasks. The research community has acquired impressive results in recent years. However, due to the deployment difficulties and performance bottlenecks, seldom these approaches are applied to the industry. In this paper, we present xASTNN, an eXtreme Abstract Syntax Tree (AST)-based Neural Network for source code representation, aiming to push this technique to industrial practice. The proposed xASTNN has three advantages. First, xASTNN is completely based on widely-used ASTs and does not require complicated data pre-processing, making it applicable to various programming languages and practical scenarios. Second, three closely-related designs are proposed to guarantee the effectiveness of xASTNN, including statement subtree sequence for code naturalness, gated recursive unit for syntactical information, and gated recurrent unit for sequential information. Third, a dynamic batching algorithm is introduced to significantly reduce the time complexity of xASTNN. Two code comprehension downstream tasks, code classification and code clone detection, are adopted for evaluation. The results demonstrate that our xASTNN can improve the state-of-the-art while being faster than the baselines.

Zepeng Liu, Zhicheng Yang, Mingye Zhu et al.

Image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception.