bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalensi Skabies pada Warga Binaan di Lembaga Pemasyarakatan Kelas IIA Tuminting Manado
Authors: Zachawerus Raul C. , Niode Nurdjannah J. , Kapantow Marlyn. G.
Published: February 2024

Abstract: Scabies is a contagious skin disease caused by the mite Sarcoptes scabiei var, hominis. The prevalence of scabies in Indonesia in 2016 is estimated at 4.6-12.9% of the 261.6 million population. Scabies ranks 3rd out of the 12 most common skin infections. This study aimed to determine the prevalence of scabies among inmates in Lembaga Pemasyarakatan kelas IIA Tuminting Manado (correctional insitution). This was a quantitative and descriptive study with a cross sectional design using The DeSkab (Deteksi Skabies) questionnaire. The results showed that 86 inmates out of a total of 190 inmates were detected of having scabies (45.3%) dominated by age 25-44 years (51.2%), middle education (76.7%), and unqualified occupancy density (100%). The most common distributions of lesions were between fingers (30.7%), followed by front part of wrist (20.2%), and elbow (13.5%). Cardinal signs of scabies were itching worse at night (98.8%), followed by lesions (86.1%), and itching complained by roommates (70.9%). In conclusion, the prevalence of scabies in Lembaga Pemasyarakatan kelas IIA Tuminting Manado is 45.3%. Inmates with detected scabies are dominated by age 25-44 years, middle education, unqualified occupancy density, distribution of lesion between fingers, and cardinal sign as itching worse at night.
Keywords: scabies; correctional institution; inmates
 
 Abstrak: Skabies merupakan penyakit kulit menular yang diakibatkan oleh tungau Sarcoptes scabiei var, hominis. Skabies menempati peringkat 3 dari 12 penyakit infeksi kulit tersering. Penelitian ini bertujuan untuk mengetahui prevalensi skabies pada warga binaan di Lembaga Pemasyarakatan kelas IIA Tuminting Manado. Jenis penelitian ialah deskriptif kuantitatif dengan desain potong lintang dan menggunakan kuesioner DeSkab (Deteksi Skabies). Hasil penelitian mendapatkan 86 warga binaan dari total 190 warga binaan yang terdeteksi skabies (45,3%), didominasi oleh usia 25-44 tahun (51,2%), tingkat pendidikan menengah (76,7%), dan kepadatan hunian yang tidak memenuhi syarat (100%). Sebaran lokasi lesi yang terbanyak ialah sela jari tangan (30,7%), diikuti pergelangan tangan depan (20,2%), dan siku luar (13,5%). Tanda kardinal skabies yang tersering muncul ialah keluhan gatal pada malam hari (98,8%), diikuti adanya bintil/lecet/borok (86,1%), dan keluhan gatal pada orang lain (sekamar) (70,9%). Simpulan penelitian ini ialah prevalensi skabies pada warga binaan Lembaga Pemasyarakatan Kelas IIA Tuminting Manado sebesar 45,3%. Warga binaan terdeteksi skabies didominasi oleh usia 25-44 tahun, tingkat pendidikan menengah, kepadatan hunian yang tidak memenuhi syarat, sebaran lokasi lesi di sela jari tangan, dan tanda kardinal skabies keluhan gatal pada malam hari.
Kata kunci: skabies; lembaga pemasyarakatan; warga binaan
Prognostics Aware Control Design for Extended Remaining Useful Life
Authors: Thuillier Julien , Jha Mayank Shekhar , Le Martelot Sebastien , Theilliol Didier
Published: February 2024

As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.
Enhancing Disaster Resilience Studies: Leveraging Linked Data and Natural Language Processing for Consistent Open-Ended Interviews
Authors: Katebi Milad , Poshdar Mani , Babaeian Jelodar Mostafa , Zihayat Kermani Morteza
Published: February 2024

Researchers have long focused on disaster resilience to mitigate calamity disruption. Disaster resilience is a complex and multi-faceted concept that is challenging to measure. Quantitative methods have traditionally been used to assess disaster resilience, but a growing interest in qualitative methods like open-ended interviews has emerged to understand experiences and perspectives. To gain deep and consistent knowledge, an open-ended interview should focus on an interviewee’s point of view and ask follow-up questions from a knowledge base that consists of relevant information; otherwise, this can lead an open-ended interview to deviate from the interviewee’s point of view to the interviewer’s point of view. In contrast to what is desired, individual interviews with last year's students in the field of civil engineering with a predefined and limited knowledge base demonstrated inconsistency in asking a follow-up question from an already existing open-ended interview. To tackle this gap, firstly, we suggest a knowledge base that can be built from peer-reviewed papers published in the disaster resilience field; secondly, we suggest a Natural Language Processing based Decision Support System using Sentence Embedding that can analyze the interviewee’s response and find resources from the knowledge base to assist the interviewer in making a consistent follow-up question
Implementation of Building Information Modeling (BIM) for Economic Sustainable Construction Minimizing Material Waste in Terms of Value Engineering
Authors: Shihata Aya Ali , Fekry Mohamed Anwar , Abbas Wessam Hamdy
Published: February 2024

The construction industry consumes a large amount of raw materials and produces large amounts of carbon dioxide emissions. However, studies have shown that philosophies alone are not efficient in solving problems in the construction industry. They must be supported by new tools and methodologies. Therefore, this study aimed to achieve a more sustainable building field by integrating BIM technology and value engineering principles in the management of building materials. to achieve the highest possible consumption of environmental resources and materials through value engineering. The methodology employed in this study was to develop a material waste management system for construction projects. Starting in the early design phase, develop a decision-making process for selecting the optimum floor tile size according to room dimensions. Some materials, such as floor tiles, wooden panels, and marble, can be used more efficiently using BIM and scheduling tools. Floor tiles are essential finishing materials in the AEC industry. The initial findings outline the benefits that can be obtained by using BIM tools to achieve waste minimization through value engineering principles by creating an automation process to choose the best floor tile size according to the space width and length and minimize the percentage of cut tiles to the total number of tiles that are used in the space. This provides a game-changing solution for construction stakeholders
Carbon Tracking in the Building Sector: A ‘CABBAGE’ Framework
Authors: Wang Jiajia , Shen Geoffrey Qiping , Xue Fan
Published: February 2024

The great challenge of global climate change urges world economies to reduce greenhouse gas emissions and promote sustainable development, where the building sector plays a vital role. Carbon tracking technology is one of the keys to capturing carbon emissions for sustainable construction such as net-zero buildings. This paper reviews five key carbon tracking technologies – life cycle assessment (LCA), energy modeling, building operation monitoring, carbon accounting software, and green certification and rating systems. With summarized advantages, beneficiaries, and limitations of the five technologies, we propose a Carbon Tracking ‘Cabbage’ (CTC) framework that incorporates all carbon tracking tools as inner technological layers for multiple stakeholders at multiple stages of construction management. The main contribution of this paper is the CTC framework that rationalizes the scopes and adoption strategies of carbon tracking technologies by collaborative stakeholders to achieve informed decision-making, implement effective carbon reduction strategies, and subsequently contribute to climate change mitigation actively
Retrofitting of Buildings to Improve Energy Efficiency: A Comprehensive Systematic Literature Review and Future Research Directions
Authors: Imani Elena , Dawood Huda , Dawood Nashwan , Occhipinti Annalisa
Published: February 2024

A large body of research has been developed with the aim of assisting policymakers in setting ambitious and achievable environmental targets for the retrofit of current and future building types for energy-efficiency and in creating effective retrofit strategies to meet these targets. The aim of this research is to conduct a comprehensive study to identify the relationship between building type and sustainability, with a particular emphasis on retrofitting and try to identify research gaps in the most effective energy-saving strategies for retrofitting various types of buildings. In this regard, this study conducts a systematic literature review (SLR) utilizes artificial intelligence (AI) and natural language processing (NLP). Sixty relevant papers are selected and reviewed, establishing a comprehensive searching scheme. The research highlights retrofitting strategies for improving energy efficiency in buildings and discuss the limitations of current practises in terms of physical and technical developments, such as utilising new energy systems and innovative retrofitting materials. To overcome these, future studies could focus on in-depth building classification, developing tailored retrofitting alternatives, and establishing an adaptive solution framework. This framework aligns cohesively with diverse typologies, adapting to changing contexts and enhancing long-term performance
A BIM-Based Approach to the Management of Historic Bridges
Authors: Biagini Carlo , Aglietti Alberto , Bongini Andrea
Published: February 2024

Building Information Modelling applied to civil infrastructure has opened up interesting scenarios for integrated management of existing infrastructural works. In the last few years Bridge Management System (BMS) have been increasingly used by infrastructure owners, based on different control systems: from stochastic methods, which make it possible to define a condition ratio (CR) starting from periodic inspections of bridges, to sensors for structural monitoring, which can originate a flow of information exchange between real artifacts and the digital model capable of activating effective reactive or planned responses in the operation and maintenance phase of the asset.
The paper intends to outline a BIM-oriented process workflow, which from the creation of parametric objects for infrastructural works using Scan-to-BIM acquisition techniques and procedures, arrives at the implementation of information bridge models to manage both static data from scheduled inspections of technicians of defects and their severity according to specific guidelines, and dynamic data from incoming and outgoing sensors placed in the physical asset for real time monitoring towards analysis, supervision and control systems of the facilities owner. The defined process workflow will be applied to some case studies, related to bridges of different characteristics, outlining some directions for future developments. In detail the research showcases the tasks undertaken and the outcomes achieved on four selected bridge case studies, which are real and situated within the geographical area of the Tuscany region, Italy. The studied bridges are all still in use and hold historical significance, as they were constructed between two hundred and one hundred years ago
As-Built Detection of Structures by the Segmentation of Three-Dimensional Models and Point Cloud Data
Authors: Yabuki Nobuyoshi , Fukuda Tomohiro , Izutsu Ryu
Published: February 2024

At construction sites, as-built management is generally conducted by taking pictures or surveying with total stations and comparing the images or survey data with design drawings or Building Information Modeling (BIM) models. Since this work is time-consuming and error-prone, more efficient and accurate methods using advanced Information and Communication Technology (ICT) are desired. Therefore, this research proposes a method that can efficiently capture the progress of construction by detecting each constructed structural member, such as beams, columns, connections, etc. In this proposed method, construction engineers first take many pictures of the construction site and conduct automatic image segmentation using a pre-trained Convolutional Neural Network (CNN) model. Next, point cloud data is generated from taken pictures by using Structure from Motion (SfM). Then, the point cloud data is semantically segmented by overlapping the segmented images and point cloud data using the pin-hole camera technique. Finally, the design BIM model and segmented point cloud data are overlapped, and constructed parts of the BIM model can be detected, which can be reported as as-built parts. A prototype system was developed and applied to an actual railway construction project in Osaka, Japan for testing the accuracy and performance of the system
A BIM-Based Framework for Facility Management Data Integration in Heritage Assets
Authors: Biagini Carlo , Aglietti Alberto , MARZI LUCA , Bongini Andrea
Published: February 2024

The operational phase of a real estate asset accounts for approximately 80% of the overall investment and management costs throughout the entire life cycle of the building, and the activities of space management and monitoring of building components and systems play a crucial role in ensuring the well-being and health of users. The AECO (Architecture, Engineering, Construction, and Operation) industry is transitioning towards a new framework governed by data-driven processes. In this context, Building Information Modeling (BIM) can support the utilization of big data generated throughout different stages of the building's life cycle, thereby establishing itself as a dynamic repository of information at the center of a constellation of systems used by a Facility Management body to achieve specific objectives (such as CAFM, ERP, BMS, etc.).
The proposed study aims to define a processing framework for the collection and management of data aimed at the implementation of DT of existing real estate assets, created based on the integration between BIM platforms and IoT technology oriented to subsequent developments of big data analytics and AI applications. The objective is to support in the operational phase of buildings the decisions of the various operators involved in planning scheduled and/or corrective maintenance actions and to generate content, recommendations, best practices by formulating predictive analysis on managed assets. In particular, a critical analysis is made of the various approaches available for the definition of an IT architecture to support IoT reference models, which will find application in the monitoring of some existing assets of the University of Florence's real estate managed by the Building Area, digitally implemented on a BIM platform. The contribution is part of a broader research activity carried out as part of the PNR Project, "BIM2DT. BIM-to-Digital Twin: information management to support decision-making in the building life cycle."
Enhancing the Realism of Virtual Construction Safety Training: Integration of Real-Time Location Systems for Real-World Hazard Simulations
Authors: Speiser Kilian , Hong Kepeng , Teizer Jochen
Published: February 2024

In numerous studies, virtual training for construction safety has been proposed as a promising approach. However, creating realistic training scenarios requires significant resources, encompassing various elements such as sound, graphics, agent behavior, and realistic hazards. Digital Twins have revolutionized this process, and although so far, on a conceptual level only, significantly reducing the associated workload, it is still not exploiting its full potential. In this work, we propose a novel approach that leverages Real-time Location Systems (RTLS) data to simulate the real-world behavior of construction workers and equipment within Virtual Training Environments (VTEs). We aim to create training scenarios with dynamic real-world instead of hardcoded made-up hazardous events. To achieve this, we propose an extension to our Digital Twin for Construction Safety (DTCS) framework that now integrates (a) trajectory data streams of construction personnel and equipment and (b) technical specifications of the construction site work environment, including location and geometry of terrain and surface objects, to simulate real-world hazards in virtual safety training scenarios. Our further contribution is a case study application to explore the DTCS training capacity. Applying a logical filtering algorithm, we can process the RTLS data and ensure that the movements of the workers and equipment within the virtual environment are as realistic and representative as within the real world. This then enables the creation of realistic hazards that trainees can encounter in the training phase. Preliminary results with trainees suggest that the proposed work can have a high potential to enhance the realism of safety training, especially when they need to experience human-machine-related interactions safely. However, further work is required to create more responsive learning environments where the equipment follows real trajectories but also responds intelligently to the trainees' actions. By leveraging real-time data and advanced visualization technologies, we bridge the gap between the physical and virtual realms, enabling trainees to interact and navigate within a realistic virtual environment
Visibility Enhancement of Crane Operators Using BIM-Based Diminished Reality
Authors: Eskandari Roghieh , Motamedi Ali
Published: February 2024

The limited visibility experienced by crane operators in construction sites poses significant challenges, leading to reduced performance and safety concerns. Obstructive elements, such as existing buildings, construction elements, or vehicles, can block the crane operator's field of view, hindering their ability to execute lifting operations with precision and confidence. To address this issue, this study presents a novel approach using Building Information Modelling (BIM)-based diminished reality (DR) to enhance visibility by dynamically removing obstructive objects from the crane operator's perspective in real-time. The research employs a marker-based registration system that effectively aligns BIM data with the physical environment, ensuring realistic and precise DR visualization. Additionally, a semi-automatic selection method that involves minimal intervention from the user is employed to select desired objects. To generate the background, the system utilizes real-time observation data from occluded areas. A validation through a case study demonstrates the practical applicability of the developed system in real-life construction scenarios
Evaluation of Computer Vision-Aided Multimedia Learning in Construction Engineering Education
Authors: Yusuf Anthony , Afolabi Adedeji , Akanmu Abiola , Olayiwola Johnson
Published: February 2024

Due to the practice-oriented nature of construction engineering education and barriers associated with physical site visits, videos are invaluable means to expose students to practical curricula content. Prior studies have investigated various design principles of multimedia pedagogical tools to enhance student learning and reduce cognitive load. These design principles and computer vision techniques can afford the design and usage of a multimedia learning environment with annotated content to teach students construction safety practices. Hence, using subjective and objective measures such as self-reported cognitive load, eye tracking metrics and verbal feedback, this study assesses the effectiveness of a computer vision-aided multimedia learning environment as well as examines variations across students’ demographics. Students were exposed to both annotated and unannotated versions of the learning environment. The annotated version of the learning environment was considered more effective in triggering students’ attention to learning content, but higher cognitive load levels were reported by participants. The same demographic groups that dwelled longer and on more annotated areas of interest also reported higher overall cognitive load. Keeping with individual differences principle of multimedia learning, demographic variations in participants' cognitive load and effectiveness of the learning environment were reported. The study provides implications for instructors in construction engineering programs on effective use of computer vision-aided annotated videos as instructional materials. This study could serve as a benchmark for future studies on artificial intelligence techniques for signaling in multimedia learning. This study reveals the affordances of computer vision-aided multimedia learning in construction engineering education and the need for adaptation of multimedia learning tools to students’ demographics
iSafeIncentive: Transforming Construction Safety Culture through Blockchain Incentives
Authors: Soltani Mehrtash , Kim Dohyeong , Pedro Akeem , Yang Jaehun , Tran Si , Lee Doyeop , Park Chansik
Published: February 2024

A significant challenge has long persisted in the construction industry: the lack of a robust incentive system to encourage and motivate workers to prioritize safety. While safety culture has been recognized as crucial, traditional approaches to incentivizing safe behaviours often encounter roadblocks, such as heavy documentation processes, recognition delays, and resource allocation difficulties. This paper addresses this problem by introducing an innovative approach to incentivize and cultivate a safety culture in the construction industry. iSafeincentive integrates blockchain technology and computer vision to develop a novel solution revolutionizing safety monitoring and incentive distribution. Computer vision technology is employed for real-time analysis of safety conditions based on on-site images, ensuring the immediate identification of safe practices. Simultaneously, blockchain technology safeguards the incentive distribution process's integrity and transparency, addressing traditional methods' shortcomings. The findings suggest that iSafeincentive offers an efficient and secure method for rewarding safe activities among workers. Furthermore, the integrated platform offers a promising pathway to enhance job site safety practices, ultimately reducing accidents and incidents within the construction sector
A Value Stream Mapping Approach to the Identification of Lean Management Opportunities for Off-Site Construction Production: A Case of Reinforced Concrete Slabs
Authors: Chen Zhe , Guo Zhengting , Xue Fan
Published: February 2024

Off-site construction (OSC), including prefabrication and Modular-integrated Construction (MiC), is gaining popularity as a school of sustainable construction methods that can improve productivity, quality, and waste reduction. However, OSC projects face challenges related to technical integration, collaboration among stakeholders, and dynamic uncertainties. As a result, high-quality standards throughout the manufacturing process of OSC products are difficult to ensure, leading to costly rework, delays, and safety issues. This paper applies a value stream mapping (VSM) approach based on lean principles to OSC production for identifying lean management opportunities for off-site construction production. The case studied in this paper is reinforced concrete slabs. First, we employed a combination of field investigations and interviews to formalize the flow of materials and information for the case. Then, VSM processed the flow for the current state map, which highlights twelve opportunities to prioritize for slab production, e.g., the adoption of digital technology (VR, BIM) in information flows. The findings in value-added activities improvement of the opportunities demonstrate the potential of lean management in the slab production case. Furthermore, the VSM approach in this paper can identify the ‘wastes’ in lean theory, which are the control points of OSC production, for enhancing quality, efficiency, and resource utilization. The findings contribute to the existing body of knowledge by providing empirical evidence of the VSM approach to the identification of lean management opportunities for OSC production
Data-Driven Construction and Operating Cost Decision Support Through Techno-Economic Analysis: Residential Case Study
Authors: Karaiskos Panos , Sulbaran Tulio
Published: February 2024

Construction and operating costs of residential buildings are important. Because, it can help designers, builders, owners, and renters make informed decisions about where and what to buy or rent. One of the most significant operating costs of residences is energy cost. More specifically, heating, ventilation, and air conditioning account for as much as 35% of the overall energy consumption of buildings in the world. Thus, the problem that this research paper addresses is the decision trade-off of construction costs vs. operating costs. Therefore, this paper aims to perform a techno-economic analysis of exterior residential wall-type alternatives in a warm-humid climate. The research followed a quantitative methodology using a virtual case study with multi-objective analysis. The results of this study show the significant importance of the building’s infiltration on the operational savings and the return on investment (ROI) of the different types of exterior residential walls. and emphasizes the importance of a holistic approach to energy conservation regulations. The novelty of this study is the emphasis on the importance of infiltration in pre-construction decision-making. The broader impact of this result is that the International Energy Conservation Code (IECC) and similar standards could be revised to reduce energy consumption and reduce greenhouse gas emissions produced during energy generation
E-Procurement in the Australian Construction Industry: Benefits, Barriers, and Adoption
Authors: Hayden Eleanor , Chan Melissa , van Kan David , Arowoiya Victor , Mohamed Mohd. Amizan
Published: February 2024

A clear benefit of e-procurement technology in the construction sector is its capacity for in reducing waste and costs. Despite its successful adoption in other major industries, the take-up of e-procurement in the construction industry has generally been slow. A variety of barriers to adoption have been identified in literature, predominantly at an international level. Whilst the benefits of e-procurement are well-known and the challenges and barriers to the adoption of these systems in the construction industry has been well-documented, research into actual outcomes following the adoption of e-procurement systems through a case study analysis is limited. Tracking and measurement of adoption rates in the Australian construction industry is particularly scarce. To build upon and add to the existing body of contemporary literature, this study seeks to examine the adoption of e-procurement technologies in the Australian construction industry. As the Australian construction sector enters a period of high inflation, technologies such as e-procurement have a critical role in mitigating these price escalations. Understanding the barriers and opportunities for wider adoption of e-procurement in the Australian construction industry is also a clear benefit with its capacity for digital transformation in the construction sector
Improving BIM Authoring Process Reproducibility with Enhanced BIM Logging
Authors: Jang Suhyung , Lee Ghang
Published: February 2024

This paper presents an enhanced BIM logger designed to capture both geometry and attribute changes of building element geometries, thereby offering a transparent source of representation of the BIM authoring process. The authors developed the logger and reproduction algorithm using the Revit C# API based on the analysis of information required to define building elements and associated attributes. The enhanced BIM log was evaluated through a case study of Villa Savoye designed by Le Corbusier. Despite negligible discrepancies, the results show that the enhanced BIM log can accurately represent the BIM authoring process capturing and reproducing 92.45% of the building elements from the original BIM model. Future research can focus on expanding the scope of logging and probing the potential of automating the BIM authoring process using these enhanced BIM logs
Multi-Robot Federated Edge Learning Framework for Efficient Coordination and Information Management in Smart Construction
Authors: Liu Xinqi , Wang Jihua , Huang Ruopan , Pan Wei
Published: February 2024

Smart construction involves a growing array of devices that generate extensive data, capable of enhancing construction efficiency and productivity. Nonetheless, the handling of this diverse and abundant information, along with the geographical spread of construction sites, poses challenges to effective communication and information processing within the management system. Multi-robot systems, as a new type of Internet of Things device, have the potential ability to coordinate workers to complete their work while serving as an edge node for information storage and processing. This paper presents a multi-robot federated edge learning framework that facilitates construction information management and communication. The work demonstrates the role of distributed databases in processing information during project execution, in contrast to centralized information systems. To address the intricacies of construction sites and the wide array of equipment involved, unmanned aerial vehicles and quadruped robots are employed as edge nodes. The formation of a federated edge learning framework ensures the real-time processing of massive data and data privacy issues. The Federated Multi-Robot (FedMR) framework is a global sharing model focused on preserving differential privacy protection. This framework is distributed to multiple edge robots in each round, enabling local real-time processing of robot tasks. The system can accomplish target detection and tracking of workers based on computer vision. Additionally, we collect MiC energy consumption data during the construction process and predict carbon emissions. Based on the implementation and testing of the system, it has been shown to provide structured and reliable information, fast local transmission, and the ability to process information in real-time. The system's ability to coordinate workers and process information makes it a valuable tool in smart construction
Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker
Authors: Park Minsoo , Lee Seungsoo , Choi Woonggyu , Jeon Yuntae , Quoc Tran Dai , Park Seunghee
Published: February 2024

Fall from height (FFH) is one of the major causes of injury and fatalities in construction industry. Deep learning-based computer vision for safety monitoring has gained attention due to its relatively lower initial cost compared to traditional sensing technologies. However, a single detection model that has been used in many related studies cannot consider various contexts at the construction site. In this paper, we propose a deep learning-based pose estimation approach for identifying potential fall hazards of construction workers. This approach can relatively increase the accuracy of estimating the distance between the worker and the fall hazard area compared to the existing methods from the experimental results. Our proposed approach can improve the robustness of worker location estimation compared to existing methods in complex construction site environments with obstacles that can obstruct the worker's position. Also, it is possible to provide information on whether a worker is aware of a potential fall risk area. Our approach can contribute to preventing FFH by providing access information to fall risk areas such as construction site openings and inducing workers to recognize the risk area even in Inattentional blindness (IB) situations
Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring
Authors: Lee Seungsoo , Son Seongwoo , Aung Pa Pa Win , Park Minsoo , Park Seunghee
Published: February 2024

According to the Ministry of Manpower, falling and slipping accidents are one of the most common accidents in addition, falls from heights (FFH), including accidents during scaffolding work, are still a major cause of death in the construction industry. Regular safety checks are currently being carried out on construction sites, but scaffold-related accidents continue to occur. Sensing technology is being attempted in many industrial sites for safety monitoring, but there are still limitations in terms of the cost of sensors and object detection, which are limited to certain risks. Therefore, this paper proposes a deep learning-based pose estimation approach to identify the risk of falling during scaffolding work in the construction industry. Through analysis of the correlation between unstable behavior during scaffold work and the angle of keypoints of workers, the proposed approach demonstrates the ability to detect the risk of falling. The proposed approach can prevent falling accidents not only by detecting construction site workers, but also by detecting specific risky behaviors. In addition, in limited work environments other than scaffolding work, the information on unstable behavior can be provided to safety managers who may not be aware of the risk, thus contributing to preventing falling accidents
Automated Extraction of Bridge Gradient from Drawings Using Deep Learning
Authors: Bayer Hakan , Faltin Benedikt , König Markus
Published: February 2024

Digital methods such as Building Information Modeling (BIM) can be leveraged, to improve the efficiency of maintenance planning of bridges. However, this requires digital building models, which are rarely available. Consequently, these models must be created retrospectively, which is time-consuming when done manually. Naturally, there is a great interest in the industry to automate the process of retro-digitization. This paper contributes to these efforts by proposing a multistage pipeline to automatically extract the gradient of a bridge from pixel-based construction drawings using deep learning. The bridge gradient, a key element of the structure’s axis, is critical for describing the elevation profile and axis slope. This information is implicitly contained in the longitudinal view of bridge drawings as gradient symbols. To extract this information, the well-established object detection model YOLOv5 is employed to locate the gradient symbols inside the drawings. Subsequently, EasyOCR and heuristic rules are applied to extract the relevant gradient parameters associated with each detected symbol. The extracted parameters are then exported in a machine-interpretable format to facilitate seamless integration into other applications. The results show a promising 98% accuracy in symbol detection and an overall accuracy of 70%. Consequently, the pipeline represents a significant advance in automating the retro-digitization process for existing bridges by reducing the time and effort required
Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach
Authors: Akanmu Abiola , Afolabi Adedeji , Okunola Akinwale
Published: February 2024

Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation
Enhancing Interactions in Augmented Reality for Construction Sites: Introducing the Archi Ontology
Authors: Farghaly Karim , Amin Khalid , Mills Grant , Wilson Duncan
Published: February 2024

Augmented reality (AR) systems offer new possibilities for enhancing how people interact with information and their environment in the construction sector. However, traditional software-driven approaches to AR system design have limitations in creating intuitive user experiences. This research presents a new user-centric framework and ontology for BIM-AR system development focused on human needs and perspectives. The BIM-AR Framework consists of a 5-step circular hybrid process with the user at the center. To enable knowledge sharing, the Augmented Reality Computer-Human Interaction (ARCHI) ontology was developed using Protégé based on established design principles. Initial validation indicates the framework's potential for improved AR system design, but further expert review and case studies are needed. The ontology also requires additional refinement and linkage to open data. This pioneering research lays the groundwork for next-generation AR systems that emphasize usability by taking a human-focused approach. With rigorous validation and evolution, the framework and ontology could transform AR technology development to create more purpose-driven and adopted solutions. This research represents a paradigm shift to user-centric AR system design that has significant potential to improve how augmented reality enhances construction project management
Image Segmentation Applied to Urban Surface and Aerial Constraints Analysis
Authors: Trani Marco Lorenzo , Madaschi Federica
Published: February 2024

The rapid progress of artificial intelligence (AI) has prompted the exploration of its potential applications in the construction industry, although at a slower rate. Since the starting point of a design is the analysis of the site’s constraints, the purpose of the ongoing research is the application of artificial intelligence in risk assessment for site areas. The primary objective of this research project is to develop an interactive map that employs AI to identify potential surface and aerial interferences. This map aims to support planners, engineers, and architects during the site context analysis phase by providing real-time visualization of obstacles. The interactive map allows users to explore and analyze identified obstacles, enabling cluster markers and filtering of features. The results obtained from applying this approach in Milan, Italy, demonstrate its functionality and usability, highlighting the tool's ability to provide valuable information in both localized and citywide scenarios. Potential improvements such as size assessment and advanced marker generation are also being examined to enhance the management of surface and air interferences. The goal is to enhance the tool's functionality, accuracy, and planning efficiency in construction projects
Reflecting Users’ Physical Characteristics in Spatial Visualization
Authors: Chae Sumin , Jo Hayoung , Kim Bomin , Lee Jin-Kook
Published: February 2024

This paper aims to quickly and precisely visualize remodeled design images based on image generation AI so that they can be used as alternative images in the early stages of design. In order to create a space image suitable for the user, the contents of the text are proceeded as follows. Bathrooms with many accidents in the space were selected as the target space, and users were designated as elderly people with many physical changes. Learning image data for additional training was self-generated according to the user's body characteristics, and the learning data focused on musculoskeletal aging among the body characteristics of elderly users. When the image was generated using additional training models, it was confirmed that a meaningful spatial image was created for musculoskeletal aging users, and it can be expected that the spatial image for spatial remodeling can be obtained quickly and accurately without the help of experts through subsequent studies to make it easier for general users
Integrating Green Roofs into Building Information Modeling (BIM): A Computational Approach for Sustainable Building Design
Authors: Cascone Stefano
Published: February 2024

The construction industry is currently witnessing a transformative period characterized by the convergence of the green and digital transitions. The green transition seeks to address environmental challenges such as climate change and resource depletion, while the digital transition leverages advanced technologies to enhance construction processes. This paper specifically explores the integration of green roofs, as component of sustainable buildings, into the Building Information Modeling (BIM) framework, a key enabler of the digital transition. Green roofs, known for their environmental benefits, consist of layers that contribute to energy efficiency, stormwater management, and biodiversity enhancement. To optimize their design and performance, this research employs Dynamo Visual Programming Language (VPL) within Autodesk Revit to create parametric models of green roofs. These models facilitate the evaluation of thermal and structural characteristics under varying water content conditions (dry and saturated). Results reveal that the choice of substrate and drainage materials significantly impacts thermal resistance, particularly in dry conditions. However, in saturated conditions, the influence on thermal performance converges, emphasizing the importance of structural considerations in both scenarios. The research also highlights various limitations and outlines avenues for future studies, including expanding the range of materials, exploring additional performance metrics, and incorporating AI and machine learning techniques. By addressing these aspects, this research contributes to a comprehensive understanding of the integration of green roofs and BIM. It provides designers and researchers with a practical tool for optimizing green roof designs, aligning with contemporary sustainable construction practices, and promoting the holistic development of green buildings
Exploration of Phosphate Soluting Bacteries Located Near Rubber Plant (Hevea brasiliensis) Field on Different Topography
Authors: Rinata Agung , Himawan Achmad , Nanik Kristalisasi Elisabeth
Published: February 2024

The exploration of phosphate-solubilizing bacteria in the root-soil of rubber plants was conducted to determine their beneficial effects. This research aimed to identify the sampling locations of rubber plantations where phosphate solubilizing bacteria are suspected to be present, isolate and identify colonies of these bacteria, assess their ability to break down P elements from Pikovskaya media and determine their genus. The research took place at the INSTIPER Yogyakarta Central Laboratory, Kec. Maguwoharjo, Kab. Sleman from February to April 2022. A descriptive method was employed, involving surveying rubber plants in different topographies, sterilizing tools and materials, collecting soil samples from rubber plants, and isolating and identifying bacteria. The research analysis included macroscopic and microscopic observations and measuring the diameter of the transparent zone. Phosphate solubilizing bacteria were found in the sampling locations of Karanggondang Village and Popongan Village. Macroscopic observations revealed 17 isolates with bacterial color morphology ranging from milky white, transparent white, to yellow. The average diameter of the transparent zone, from highest to lowest, was as follows: B5(4)2A (18.3 mm), A1(4)1B (14.3 mm), A1(4)2A (14 mm), and C1(3)1 (14 mm). The phosphate solubilizing bacteria isolate A1(4)2A was identified as the Escherichia Genus while isolating C1(3)1 was identified as the Genus Acetobacter.
Symptom assessment in cancer patients using the Memorial Symptom Assessment Scale
Authors: Kumbasar Seda , Basat Sema
Published: February 2024

Objective: Cancer is a chronic disease characterized by the unregulated proliferation and abnormal spread of cells. Symptoms that occur after chemotherapy treatment, which is the most commonly used treatment in cancer, negatively affect morbidity, effectiveness of treatment and quality of life. This study aimed to determine symptoms in cancer patients undergoing chemotherapy using the Memorial Symptom Assessment Scale (MSAS)
Material and Methods: A total of 273 cancer patients who applied to Ümraniye Training and Research Hospital emergency department as outpatients and were hospitalized in the internal medicine department were included in the study between 5/1/2014 and 6/4/2014. The study was conducted prospectively. Of the 273 participants, 41.3% were female (N = 112) and 58.7% were male (N = 159). The mean age of the participants was 62.46 ± 12.913 years (range, 32-92 years). Participants were divided into 6 groups based on diagnosis: Lung cancers, gastrointestinal cancers, cancers of the reproductive system, hematologic cancers, cancers of the urinary system and others. Each participant used the Memorial Symptom Assessment Scale (MSAS) containing 32 symptoms to rate whether they had experienced each symptom during the past week. Of these 32 items, 24 provide information on the intensity and frequency of symptoms, while 8 provide information on severity and distress.
Results: The most common types of symptoms based on the Memorial Symptom Rating Scale were lack of energy (72.9%), pain (71.1%) and difficulty concentrating (60.8%), while the least common symptoms were feeling “I don’t look like myself” (4.4%), swelling of arms and legs and changes in skin (7.3%) and constipation (8.4%).  
Conclusion: Cancer patients have a lower quality of life due to the side effects of the treatments they receive or the impact of the cancer itself. The most common symptoms in the patients screened in this study were lack of energy and pain. In light of these data, determining the factors affecting the quality of life in cancer patients may be guiding in determining the symptomatic treatment for these patients and facilitating patient care.