Hasil untuk "Construction industry"

Md Istiauk Hossain Rifat, Moin Khan, Mohammad Zunaed

The textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs. To address this, we propose a real-time Non-Intrusive Load Monitoring (NILM)-based framework tailored for industrial applications, with a focus on identical motor-driven loads representing textile cutting machines. A hardware setup comprising voltage and current sensors, Arduino Mega and ESP8266 was developed to capture aggregate and individual load data, which was stored and processed on cloud platforms. A new dataset was created from three identical induction motors and auxiliary loads, totaling over 180,000 samples, to evaluate the state-of-the-art MATNILM model under challenging industrial conditions. Results indicate that while aggregate energy estimation was reasonably accurate, per-appliance disaggregation faced difficulties, particularly when multiple identical machines operated simultaneously. Despite these challenges, the integrated system demonstrated practical real-time monitoring with remote accessibility through the Blynk application. This work highlights both the potential and limitations of NILM in industrial contexts, offering insights into future improvements such as higher-frequency data collection, larger-scale datasets and advanced deep learning approaches for handling identical loads.

Ricardo O'Nascimento, Bruna Petreca, Morag Seaton et al.

The fashion industry faces urgent challenges related to overconsumption, material waste, and consumer detachment from garment lifecycles. While circular economy (CE) principles offer a promising alternative, strategies that actively engage consumers in circular practices remain underexplored. This study presents the Circular Shirt Builder (CSB), a physical apparel configurator designed to promote circular behaviours through modular garment design and embodied customisation. Using a Living Lab methodology, 19 participants engaged with the CSB in a stakeholder engagement platform in a retail-like setting, assembling modular shirts from a predefined library of components. The study employed a dual analysis approach: inductive thematic analysis and a deductive evaluation using the wellbeing framework for consumer experiences in the circular economy of the textile industry. Findings suggest that the CSB can foster emotional attachment, support learning about garment construction, encourage creative self-expression, and prompt reflection on consumption habits. Several wellbeing dimensions, such as playfulness, agency, and prospective thinking, appeared to be activated through the hands-on interaction. This research indicated that configurator tools grounded in circular and wellbeing principles may support long-term product use, more mindful consumption, and greater consumer involvement in transitions toward a circular textile economy.

T. Q. D. Pham, K. D. Tran, Khanh T. P. Nguyen et al.

As global industries transition towards Industry 5.0 predictive maintenance PM remains crucial for cost effective operations resilience and minimizing downtime in increasingly smart manufacturing environments In this chapter we explore how the integration of Federated Learning FL and blockchain BC technologies enhances the prediction of machinerys Remaining Useful Life RUL within decentralized and human centric industrial ecosystems Traditional centralized data approaches raise concerns over privacy security and scalability especially as Artificial intelligence AI driven smart manufacturing becomes more prevalent This chapter leverages FL to enable localized model training across multiple sites while utilizing BC to ensure trust transparency and data integrity across the network This BC integrated FL framework optimizes RUL predictions enhances data privacy and security establishes transparency and promotes collaboration in decentralized manufacturing It addresses key challenges such as maintaining privacy and security ensuring transparency and fairness and incentivizing participation in decentralized networks Experimental validation using the NASA CMAPSS dataset demonstrates the model effectiveness in real world scenarios and we extend our findings to the broader research community through open source code on GitHub inviting collaborative development to drive innovation in Industry 5.0

Kai Zhang, Zekai Zhang, Xihe Sun et al.

Automatic vision inspection holds significant importance in industry inspection. While multimodal large language models (MLLMs) exhibit strong language understanding capabilities and hold promise for this task, their performance remains significantly inferior to that of human experts. In this context, we identify two key challenges: (i) insufficient integration of anomaly detection (AD) knowledge during pre-training, and (ii) the lack of technically precise and context-aware language generation for anomaly reasoning. To address these issues, we propose ADSeeker, an anomaly task assistant designed to enhance inspection performance through knowledge-grounded reasoning. ADSeeker first leverages a curated visual document knowledge base, SEEK-M&V, which we construct to address the limitations of existing resources that rely solely on unstructured text. SEEK-M\&V includes semantic-rich descriptions and image-document pairs, enabling more comprehensive anomaly understanding. To effectively retrieve and utilize this knowledge, we introduce the Query Image-Knowledge Retrieval-Augmented Generation Q2K RAG framework. To further enhance the performance in zero-shot anomaly detection (ZSAD), ADSeeker leverages the Hierarchical Sparse Prompt mechanism and type-level features to efficiently extract anomaly patterns. Furthermore, to tackle the challenge of limited industry anomaly detection (IAD) data, we introduce the largest-scale AD dataset, Multi-type Anomaly MulA, encompassing 72 multi-scale defect types across 26 categories. Extensive experiments show that our plug-and-play framework, ADSeeker, achieves state-of-the-art zero-shot performance on several benchmark datasets.

Rafael da Silva Maciel, Lucio Veraldo

The evolution of the 5S methodology with the support of artificial intelligence techniques represents a significant opportunity to improve industrial organization audits in the automotive chain, making them more objective, efficient and aligned with Industry 4.0 standards. This work developed an automated 5S audit system based on large-scale language models (LLM), capable of assessing the five senses (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) in a standardized way through intelligent image analysis. The system's reliability was validated using Cohen's concordance coefficient (kappa = 0.75), showing strong alignment between the automated assessments and the corresponding human audits. The results indicate that the proposed solution contributes significantly to continuous improvement in automotive manufacturing environments, speeding up the audit process by 50% of the traditional time and maintaining the consistency of the assessments, with a 99.8% reduction in operating costs compared to traditional manual audits. The methodology presented establishes a new paradigm for integrating lean systems with emerging AI technologies, offering scalability for implementation in automotive plants of different sizes.

Al-Kazee Mohamed Faisal, Mahdavinejad Mohammadjavad, Ramhamdani Racha et al.

Nowadays, Building Information Modeling (BIM) is one of the most significant technologies for most firms in the construction industry of developing countries. Building information Modeling has become essential for facilitating all processes throughout the building lifecycle. However, a glance at the construction environment in developing countries shows that the implementation of BIM lags behind its potential due to various challenges at the project or organizational level. Therefore, this research aims to identify, classify, and compile the barriers associated with BIM implementation in the building industry of developing countries. The research methodology is content analysis, combining meta-analysis with qualitative and quantitative approaches to address the research question. Twenty-eight barriers were identified in two staged content analysis processes. The research outcomes form a comprehensive perspective for enhancing awareness among project stakeholders at different levels. The results highlight that most barriers were related to employee skills, a lack of standards, and software interoperability. The conclusion equips design and construction firms in developing countries with the strategies to effectively address challenges while developing their BIM implementation plans.

Martin Detloff

The rapid evolution of software development necessitates enhanced safety measures. Extracting information about software failures from companies is becoming increasingly more available through news articles. This research utilizes the Failure Analysis Investigation with LLMs (FAIL) model to extract industry-specific information. Although the FAIL model's database is rich in information, it could benefit from further categorization and industry-specific insights to further assist software engineers. In previous work news articles were collected from reputable sources and categorized by incidents inside a database. Prompt engineering and Large Language Models (LLMs) were then applied to extract relevant information regarding the software failure. This research extends these methods by categorizing articles into specific domains and types of software failures. The results are visually represented through graphs. The analysis shows that throughout the database some software failures occur significantly more often in specific industries. This categorization provides a valuable resource for software engineers and companies to identify and address common failures. This research highlights the synergy between software engineering and Large Language Models (LLMs) to automate and enhance the analysis of software failures. By transforming data from the database into an industry specific model, we provide a valuable resource that can be used to identify common vulnerabilities, predict potential risks, and implement proactive measures for preventing software failures. Leveraging the power of the current FAIL database and data visualization, we aim to provide an avenue for safer and more secure software in the future.

Houssam Razouk, Leonie Benischke, Daniel Garber et al.

The extraction of causal information from textual data is crucial in the industry for identifying and mitigating potential failures, enhancing process efficiency, prompting quality improvements, and addressing various operational challenges. This paper presents a study on the development of automated methods for causal information extraction from actual industrial documents in the semiconductor manufacturing industry. The study proposes two types of causal information extraction methods, single-stage sequence tagging (SST) and multi-stage sequence tagging (MST), and evaluates their performance using existing documents from a semiconductor manufacturing company, including presentation slides and FMEA (Failure Mode and Effects Analysis) documents. The study also investigates the effect of representation learning on downstream tasks. The presented case study showcases that the proposed MST methods for extracting causal information from industrial documents are suitable for practical applications, especially for semi structured documents such as FMEAs, with a 93\% F1 score. Additionally, MST achieves a 73\% F1 score on texts extracted from presentation slides. Finally, the study highlights the importance of choosing a language model that is more aligned with the domain and in-domain fine-tuning.

Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley et al.

This paper investigates the relationship between scientific innovation in biomedical sciences and its impact on industrial activities, focusing on how the historical impact and content of scientific papers influenced future funding and innovation grant application content for small businesses. The research incorporates bibliometric analyses along with SBIR (Small Business Innovation Research) data to yield a holistic view of the science-industry interface. By evaluating the influence of scientific innovation on industry across 10,873 biomedical topics and taking into account their taxonomic relationships, we present an in-depth exploration of science-industry interactions where we quantify the temporal effects and impact latency of scientific advancements on industrial activities, spanning from 2010 to 2021. Our findings indicate that scientific progress substantially influenced industrial innovation funding and the direction of industrial innovation activities. Approximately 76% and 73% of topics showed a correlation and Granger-causality between scientific interest in papers and future funding allocations to relevant small businesses. Moreover, around 74% of topics demonstrated an association between the semantic content of scientific abstracts and future grant applications. Overall, the work contributes to a more nuanced and comprehensive understanding of the science-industry interface, opening avenues for more strategic resource allocation and policy developments aimed at fostering innovation.

Kurukulasooriya Fernando ana Gianluca Demartini

Recent advancements of generative LLMs (Large Language Models) have exhibited human-like language capabilities but have shown a lack of domain-specific understanding. Therefore, the research community has started the development of domain-specific LLMs for many domains. In this work we focus on discussing how to build mining domain-specific LLMs, as the global mining industry contributes significantly to the worldwide economy. We report on MiningGPT, a mining domain-specific instruction-following 7B parameter LLM model which showed a 14\% higher mining domain knowledge test score as compared to its parent model Mistral 7B instruct.

Juliette Grosset, Alain-Jérôme Fougères, M Djoko-Kouam et al.

One of the challenges of Industry 4.0, is to determine and optimize the flow of data, products and materials in manufacturing companies. To realize these challenges, many solutions have been defined such as the utilization of automated guided vehicles (AGVs). However, being guided is a handicap for these vehicles to fully meet the requirements of Industry 4.0 in terms of adaptability and flexibility: the autonomy of vehicles cannot be reduced to predetermined trajectories. Therefore, it is necessary to develop their autonomy. This will be possible by designing new generations of industrial autonomous vehicles (IAVs), in the form of intelligent and cooperative autonomous mobile robots.In the field of road transport, research is very active to make the car autonomous. Many algorithms, solving problematic traffic situations similar to those that can occur in an industrial environment, can be transposed in the industrial field and therefore for IAVs. The technologies standardized in dedicated bodies (e.g., ETSI TC ITS), such as those concerning the exchange of messages between vehicles to increase their awareness or their ability to cooperate, can also be transposed to the industrial context. The deployment of intelligent autonomous vehicle fleets raises several challenges: acceptability by employees, vehicle location, traffic fluidity, vehicle perception of changing environments (dynamic), vehicle-infrastructure cooperation, or vehicles heterogeneity. In this context, developing the autonomy of IAVs requires a relevant working method. The identification of reusable or adaptable algorithms to the various problems raised by the increase in the autonomy of IAVs is not sufficient, it is also necessary to be able to model, to simulate, to test and to experiment with the proposed solutions. Simulation is essential since it allows both to adapt and to validate the algorithms, but also to design and to prepare the experiments.To improve the autonomy of a fleet, we consider the approach relying on a collective intelligence to make the behaviours of vehicles adaptive. In this chapter, we will focus on a class of problems faced by IAVs related to collision and obstacle avoidance. Among these problems, we are particularly interested when two vehicles need to cross an intersection at the same time, known as a deadlock situation. But also, when obstacles are present in the aisles and need to be avoided by the vehicles safely.

Yongshun Xu, Ming Chi, Heap-Yih Chong et al.

Building information modeling (BIM) and blockchain applications have introduced significant benefits to the architecture, engineering, construction, and operation (AECO) industry in recent years. Although publications on BIM and blockchain integration have been increasing, no systematic examination of the present status and managerial implications of integrated BIM and blockchain has been conducted. To bridge this gap, this paper conducts a state-of-the-art review of the development of integrated BIM and blockchain in a built environment. A combination of qualitative and quantitative methods was adopted to synthesize and analyze the research evidence. The results revealed five key managerial implica­tions of BIM integration with blockchain at the project level: design and collaboration, financial management, construction management, information management, and integration management (with other cutting-edge technologies). Challenges and opportunities are outlined and articulated from both technological and managerial perspectives, such as stakeholder management, impact assessment, real-time project management, information redundancy, and incompatibility.

Ebuka Valentine Iroha, Tsunemi Watanabe, Tsuchiya Satoshi

Many studies have been conducted on the poor performance of the construction industry. Nigeria’s construction industry has been linked to project delays and cost overruns, leading to many abandoned construction projects throughout the country. These issues are largely attributed to inadequate project management practices and the underutilization of project managers. To address these challenges, an institutional analysis was conducted to examine the factors, within the institutional framework of the Nigerian construction industry, that hinder the effective utilization of project managers and the implementation of project management practices. Data were collected from the previous literature and were supported by data collected through semi-structured interviews in Nigeria. The collected data were coded into a four-level framework for institutional analysis. This method was employed to analyze the interrelationships between the identified embedded factors, institutional laws and regulations, and construction organizations, and to understand how their influence results in the underutilization of project managers. Deviation analysis was conducted as an additional method to categorize the impacts of the embedded factors at each institutional level and to determine how these impacts contribute to the underutilization of project managers in the Nigerian construction industry (NCI). It was found that the system of the underutilization of project managers consists of two subsystems: underutilization and lowering commitment. For the former subsystem, corruption, political influence, religious and tribal discrimination, and organizational culture were found to adversely influence the institutional structure of the construction industry in Nigeria. These factors weaken the governance mechanisms within the industry, leading project managers to prioritize corrupt practices over project needs. The ineffectiveness of existing laws and regulations exacerbates the situation, supporting unfair working conditions and contributing to the underperformance of project managers. This result leads to development at the top of the latter subsystem, with minimal incentives and limited opportunities for career growth within construction organizations. The findings hold significant potential for addressing systemic issues in the Nigerian construction industry, particularly the underutilization of project managers and organizational support measures to improve project management practices and mitigate the adverse effects of corruption.

Saara Tenhunen, Tomi Männistö, Petri Ihantola et al.

Previous research has demonstrated that preparing students for life in software engineering is not a trivial task. Authentic learning experiences are challenging to provide, and there are gaps between what students have done at the university and what they are expected to master when getting into the industry after graduation. To address this challenge, we present a novel way of teaching industry-relevant skills in a university-led internal software startup called Software Development Academy (SDA). In addition to describing the SDA concept in detail, we have investigated what educational aspects characterise SDA and how it compares to capstone projects. The questions are answered based on 15 semi-structured interviews with alumni of SDA. Working with production-quality software and having a wide range of responsibilities were perceived as the most integral aspects of SDA and provided students with a comprehensive skill set for the future.

Jiří Šlanhof, Aleš Průcha, Barbora Nečasová et al.

This paper deals with the issue of fastening sheet-metal components on the facades of contemporary buildings that are massively insulated with external thermal insulation composite systems. This research focused on facades with thin-layer acrylic plaster and sheet-metal components made of aluminium, copper and hot-dipped galvanized sheet metal. Two different test methods and test sample geometries were used to determine the most critical aspects for the studied application sectors. Surprisingly high tensile properties as well as shear stresses in the bonded joints were recorded for all the selected combinations. The presented results confirmed the assumed benefits for the construction industry and the future practical use of this technology in construction, although the durability of a bonded joint will always depend mainly on the quality of the bonded substrate.

Taewon Chung, Jin Hoon Bok, Hyon Wook Ji

In the field of infrastructure construction, progress in digital transformation remains limited; this is particularly true in road construction, an infrastructure facility involving design, construction, and operation stages. Many construction subjects are involved at each stage of this cycle, generating substantial construction information. To drive the digital transformation of the construction industry, a construction information classification system is necessary for the development of a systematic construction information model. This study focuses on categorizing construction information into objects and activities, defining unit work by combining these two categories, and allowing for the flexible processing of construction information. A construction information classification system was developed, representing the evolving construction-related methodological information throughout the project’s lifecycle. Applying this multi-story expandable systematic hierarchical system to a highway project demonstrates the representation of key tasks in each construction phase, enabling the future stage-specific expansion of construction information for individual tasks. The proposed system could advance the digital transformation in infrastructure construction.

Xuanle Ren, Xiaoxia Cui

Secure Element (SE) in SoC sees an increasing adoption in industry. Many applications in IoT devices are bound to the SE because it provides strong cryptographic functions and physical protection. Though SE-in-SoC provides strong proven isolation for software programs, it also brings more design complexity and higher cost to PCB board building. More, SE-in-SoC may still have security concerns, such as malware installation and user impersonation. In this work, we employ TEE, a hardware-backed security technique, for protecting SE-in-SoC and RISCV. In particular, we construct various enclaves for isolating applications and manipulating the SE, with the inherently-secure primitives provided by RISC-V. Using hardware and software co-design, the solution ensures trusted execution and secure communication among applications. The security of SE is further protected by enforcing the SE to be controlled by a trusted enclave and making the RISC-V core resilient to side-channel attacks.

Chuyi Li, Lulu Li, Hongliang Jiang et al.

For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application. Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia. Specifically, we heavily assimilate ideas from recent network design, training strategies, testing techniques, quantization, and optimization methods. On top of this, we integrate our thoughts and practice to build a suite of deployment-ready networks at various scales to accommodate diversified use cases. With the generous permission of YOLO authors, we name it YOLOv6. We also express our warm welcome to users and contributors for further enhancement. For a glimpse of performance, our YOLOv6-N hits 35.9% AP on the COCO dataset at a throughput of 1234 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 43.5% AP at 495 FPS, outperforming other mainstream detectors at the same scale~(YOLOv5-S, YOLOX-S, and PPYOLOE-S). Our quantized version of YOLOv6-S even brings a new state-of-the-art 43.3% AP at 869 FPS. Furthermore, YOLOv6-M/L also achieves better accuracy performance (i.e., 49.5%/52.3%) than other detectors with a similar inference speed. We carefully conducted experiments to validate the effectiveness of each component. Our code is made available at https://github.com/meituan/YOLOv6.

Vimal Panara, Vedang Bhonde, Shivam Patel et al.

Reuse of waste PET bottles in construction industry is emerging as a potential option for improving the properties of unreinforced concrete. Although earlier studies have explored the effect of PET reinforced concrete under monotonic loading, limited studies have explored its behaviour under cyclic loading. In this context, the present study quantifies the impact of PET strips on characteristics of concrete under low frequency cyclic loading at different stress levels. A comparative assessment of stress–strain response, deformation characteristics, loading/unloading modulus, Poisson’s ratio and strain energy density for PET macro-reinforced concrete has been performed with conventional concrete (CC). PET concrete exhibits higher loading capacity, lower variability, delay in damage propagation and better damage tolerance under cyclic loading. The study also proposes a simple equation for quantification of confinement effect developed due to the presence of PET strips in concrete. Overall, the utilization of waste PET in concrete presents a sustainable approach for improving the properties of unreinforced concrete (viz., pavement application, flowable fill application, brick manufacturing), while reducing the negative environmental impact of waste PET.

Javier Ferreiro-Cabello, Esteban Fraile-Garcia, Alpha Pernia-Espinoza et al.

The amount of ground olive stone available in Spain surpasses the needs of the construction industry for lightweight aggregate. The objective herein is to generate a material, lightweight mortar, with different percentages of ground olive stone, and then evaluate the mechanical performance and viability of these materials for the manufacture of lightweight elements used in the construction sector. To this end, an experiment was designed with nine different dosages of ground olive stone and three types of cement. In all, 378 test pieces were produced to assess the material, its handling while fresh, and its performance. Based on an analysis of consistency, density, compressive strength, and flexural strength, we were able to determine how much ground olive stone can be successfully incorporated into the material: 30% ground olive stone achieved a decrease in density of 15% compared to mortar without ground olive stone. The compressive strength of the different dosages studied remained above 70% of that of the mortar without ground olive stone. Bending behavior was more severely compromised, the values being around 50%. Cements with a more robust strength performance proved capable of assimilating a higher percentage of ground olive stone. This study shows the technical viability of the materials produced.