Pub Date : 2023-09-22DOI: 10.36680/j.itcon.2023.028
Hassan Anifowose, Kifah Alhazzaa, Manish Dixit
An important practice for reducing the effects of global warming is the design and construction of energy-efficient buildings. In design education, the full comprehension of thermal behavior in buildings based on their geometry and material composition is required. The complexity of energy simulation principles, vis-a-vis the number of elements that impact the energy loads, their linkages, and their relationships to one another all combine to make this a challenging subject to absorb. Virtual Reality (VR) provides an immersive way to learn the concepts of building energy responses; however, the development of VR applications for education is difficult due to the knowledge, skill, and performance resource-related gaps. Unoptimized VR applications can adversely impact learning if user experiences are broken due to performance lags. This research, therefore, explores VR as a teaching tool for building energy education while showcasing the development process toward a visually accurate simulation and performant application. We developed EnergySIM; a multi-user VR building energy simulation prototype of the famous Farnsworth House. Using this prototype, we document rigorously tested development workflows for improved VR game performance, high visual fidelity, and user interaction, the three key factors which positively contribute to user knowledge retention. The study combines menu-driven interaction, virtual exploration, and miniature model manipulation approaches with the aim of testing user understanding and knowledge retention. Highlighted results provide reduced barriers of entry for educators towards developing higher quality educational VR applications. EnergySIM showcases pre-simulated building exterior surface heatmaps response from four seasons (winter, summer, fall, and spring) alongside an all-year-round sun-hour scenario. Four different material pre-simulated scenarios (single glazing, double glazing, concrete, and wood) for interior atmospheric temperature mapping are also explored. Preferred interaction methods are documented by allowing users’ visual appraisal of alternative building materials based on insulation capacity or resistance to heat flow (R-value). The significance of this work lies in its potential to revolutionize how students, designers, and instructors approach building energy education in today’s world. EnergySIM provides a hands-on and visually engaging learning experience towards the enhancement of knowledge retention and understanding. It pushes the boundaries of development for visual fidelity using geometry/mesh modeling input from various software into game engines and optimizing game performance using the HTC Vive Pro Eye and Meta Quest Pro headsets.
减少全球变暖影响的一个重要做法是设计和建造节能建筑。在设计教育中，需要根据建筑的几何形状和材料组成充分理解建筑的热行为。能量模拟原理的复杂性，相对于影响能量负荷的元素的数量，它们的联系，以及它们彼此之间的关系，都使这成为一个具有挑战性的主题。虚拟现实(VR)提供了一种身临其境的方式来学习建筑能源响应的概念;然而，由于知识、技能和性能资源相关的差距，VR应用于教育的开发是困难的。如果由于性能滞后而破坏了用户体验，那么未优化的VR应用程序可能会对学习产生不利影响。因此，本研究探索了VR作为建筑能源教育的教学工具，同时展示了视觉上精确的模拟和性能应用的发展过程。我们开发了EnergySIM;著名的法恩斯沃斯之家的多用户VR建筑能源模拟原型。使用此原型，我们记录了经过严格测试的开发工作流程，以改进VR游戏性能，高视觉保真度和用户交互，这三个关键因素对用户知识保留有积极贡献。该研究结合了菜单驱动交互、虚拟探索和微型模型操作方法，目的是测试用户的理解和知识保留。突出显示的结果为教育工作者开发更高质量的教育VR应用降低了进入门槛。EnergySIM展示了四个季节(冬季、夏季、秋季和春季)的预模拟建筑外表面热图响应，以及全年的太阳小时场景。四种不同的材料预模拟场景(单层玻璃，双层玻璃，混凝土和木材)，用于室内大气温度映射也进行了探索。通过允许用户根据隔热能力或热流阻力(r值)对可选建筑材料进行视觉评估，记录了首选的交互方法。这项工作的意义在于，它有可能彻底改变当今世界学生、设计师和教师如何进行建筑能源教育。EnergySIM提供了一个动手和视觉上引人入胜的学习经验，以提高知识的保留和理解。它推动了视觉保真度的发展界限，使用几何/网格建模输入从各种软件到游戏引擎，并使用HTC Vive Pro Eye和Meta Quest Pro耳机优化游戏性能。
{"title":"ENERGYSIM: techniques for advancing building energy education through immersive virtual reality (VR) simulation","authors":"Hassan Anifowose, Kifah Alhazzaa, Manish Dixit","doi":"10.36680/j.itcon.2023.028","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.028","url":null,"abstract":"An important practice for reducing the effects of global warming is the design and construction of energy-efficient buildings. In design education, the full comprehension of thermal behavior in buildings based on their geometry and material composition is required. The complexity of energy simulation principles, vis-a-vis the number of elements that impact the energy loads, their linkages, and their relationships to one another all combine to make this a challenging subject to absorb. Virtual Reality (VR) provides an immersive way to learn the concepts of building energy responses; however, the development of VR applications for education is difficult due to the knowledge, skill, and performance resource-related gaps. Unoptimized VR applications can adversely impact learning if user experiences are broken due to performance lags. This research, therefore, explores VR as a teaching tool for building energy education while showcasing the development process toward a visually accurate simulation and performant application. We developed EnergySIM; a multi-user VR building energy simulation prototype of the famous Farnsworth House. Using this prototype, we document rigorously tested development workflows for improved VR game performance, high visual fidelity, and user interaction, the three key factors which positively contribute to user knowledge retention. The study combines menu-driven interaction, virtual exploration, and miniature model manipulation approaches with the aim of testing user understanding and knowledge retention. Highlighted results provide reduced barriers of entry for educators towards developing higher quality educational VR applications. EnergySIM showcases pre-simulated building exterior surface heatmaps response from four seasons (winter, summer, fall, and spring) alongside an all-year-round sun-hour scenario. Four different material pre-simulated scenarios (single glazing, double glazing, concrete, and wood) for interior atmospheric temperature mapping are also explored. Preferred interaction methods are documented by allowing users’ visual appraisal of alternative building materials based on insulation capacity or resistance to heat flow (R-value). The significance of this work lies in its potential to revolutionize how students, designers, and instructors approach building energy education in today’s world. EnergySIM provides a hands-on and visually engaging learning experience towards the enhancement of knowledge retention and understanding. It pushes the boundaries of development for visual fidelity using geometry/mesh modeling input from various software into game engines and optimizing game performance using the HTC Vive Pro Eye and Meta Quest Pro headsets.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.36680/j.itcon.2023.031
Carrie Sturts Dossick, Madision Snider, Laura Osburn
The adoption of Internet of Things has grown significantly in recent years both to address sustainability in campus operations and as part of digital twin systems. This study looks at in-depth cases of large university campus owners and the challenges that this IOT introduces for the maintenance and management of these systems and the data they collect. In this ethnography there are three main time orientations related to facilities management (Facilities), Information Technology (IT), and Capital Projects. First, a university campus is like a small city, with buildings, utilities, and transportation systems - taken together we call this campus infrastructure (buildings 50-100, roads and utilities 20-50 years). Second, IT employees think on 2–3-month scale, working through implementing software and hardware upgrades, configurations and patches, at times needing agile operations to deal with emerging cybersecurity threats. Third, in Capital Projects the design phase can last 9 months, and the construction from 1 - 2 years for a typical project, and this is where IOT technologies are often first introduced into campus. While the capital project teams reflect on the user experience, these teams are often removed from the realities of facilities management and do not understand the time scales or the scope of the work that is required to manage a portfolio of Facilities and IT systems. In this paper, we explore how these time orientations lead to tensions in the owners’ selection of IOT devices and systems, in the integration of new technologies into existing systems, and in the operations of keeping existing systems up and running for the longer time scales of campus infrastructure life spans. Furthermore, this paper presents a paradox: If they speed up, they lose things, if they slow down, they lose other things, and presents ways that owner organizations manage this paradox through temporal boundary spanners who understand the disciplinary requirements, cultures, and frameworks across the organization and helps to mitigate the tensions across these differences.
{"title":"Operations, IT, and construction time orientations and the challenges of implementing IOT","authors":"Carrie Sturts Dossick, Madision Snider, Laura Osburn","doi":"10.36680/j.itcon.2023.031","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.031","url":null,"abstract":"The adoption of Internet of Things has grown significantly in recent years both to address sustainability in campus operations and as part of digital twin systems. This study looks at in-depth cases of large university campus owners and the challenges that this IOT introduces for the maintenance and management of these systems and the data they collect. In this ethnography there are three main time orientations related to facilities management (Facilities), Information Technology (IT), and Capital Projects. First, a university campus is like a small city, with buildings, utilities, and transportation systems - taken together we call this campus infrastructure (buildings 50-100, roads and utilities 20-50 years). Second, IT employees think on 2–3-month scale, working through implementing software and hardware upgrades, configurations and patches, at times needing agile operations to deal with emerging cybersecurity threats. Third, in Capital Projects the design phase can last 9 months, and the construction from 1 - 2 years for a typical project, and this is where IOT technologies are often first introduced into campus. While the capital project teams reflect on the user experience, these teams are often removed from the realities of facilities management and do not understand the time scales or the scope of the work that is required to manage a portfolio of Facilities and IT systems. In this paper, we explore how these time orientations lead to tensions in the owners’ selection of IOT devices and systems, in the integration of new technologies into existing systems, and in the operations of keeping existing systems up and running for the longer time scales of campus infrastructure life spans. Furthermore, this paper presents a paradox: If they speed up, they lose things, if they slow down, they lose other things, and presents ways that owner organizations manage this paradox through temporal boundary spanners who understand the disciplinary requirements, cultures, and frameworks across the organization and helps to mitigate the tensions across these differences.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.36680/j.itcon.2023.024
O. Ogunseiju, Nihar J. Gonsalves, A. Akanmu, D. Bairaktarova, P. Agee, Kereshmeh Asfari
As the construction industry continues to advance technologically, the adoption of sensing technologies is gradually gaining momentum. Sensing technologies (such as radio frequency identification systems, laser scanners, cameras, and global position systems) play a significant role in reducing costs, improving project productivity, and enhancing workers' health and safety. This has prompted the need for a workforce with the required skills and knowledge for deploying sensing technologies in the industry. Since construction-related education is aimed at preparing students for the future of the industry, it is important to investigate the industry’s expectations for equipping the future workforce with the required skills. This study adopts a mixed-method research approach. Data are collected from surveys, case studies, and a focus group discussion with industry practitioners. The data elucidate participants’ perceptions, attitudes, and beliefs regarding: the skills required, and level of knowledge transfer required to advance sensing technologies on construction projects, and the value and anticipated demand for these skills. The findings also revealed the extent to which sensing technologies are deployed in the industry and the benefits driving the adoption of these technologies. The results reveal a high rate of adoption of sensing technologies amongst industry practitioners and inform construction applications and skills to be taught in construction engineering education. This study contributes to the existing scarce literature on the knowledge and skill demands of the industry to implement sensing technologies. The findings provide critical feedback for expanding the construction education curriculum to meet up the industry’s demand and adequately prepare the future workforce.
{"title":"Sensing technologies in construction engineering education: industry experiences and expectations","authors":"O. Ogunseiju, Nihar J. Gonsalves, A. Akanmu, D. Bairaktarova, P. Agee, Kereshmeh Asfari","doi":"10.36680/j.itcon.2023.024","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.024","url":null,"abstract":"As the construction industry continues to advance technologically, the adoption of sensing technologies is gradually gaining momentum. Sensing technologies (such as radio frequency identification systems, laser scanners, cameras, and global position systems) play a significant role in reducing costs, improving project productivity, and enhancing workers' health and safety. This has prompted the need for a workforce with the required skills and knowledge for deploying sensing technologies in the industry. Since construction-related education is aimed at preparing students for the future of the industry, it is important to investigate the industry’s expectations for equipping the future workforce with the required skills. This study adopts a mixed-method research approach. Data are collected from surveys, case studies, and a focus group discussion with industry practitioners. The data elucidate participants’ perceptions, attitudes, and beliefs regarding: the skills required, and level of knowledge transfer required to advance sensing technologies on construction projects, and the value and anticipated demand for these skills. The findings also revealed the extent to which sensing technologies are deployed in the industry and the benefits driving the adoption of these technologies. The results reveal a high rate of adoption of sensing technologies amongst industry practitioners and inform construction applications and skills to be taught in construction engineering education. This study contributes to the existing scarce literature on the knowledge and skill demands of the industry to implement sensing technologies. The findings provide critical feedback for expanding the construction education curriculum to meet up the industry’s demand and adequately prepare the future workforce.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.36680/j.itcon.2023.025
James Olaonipekun Toyin, M. Mewomo
Building Information Modelling (BIM) is a revolutionary invention within the construction industry that essentially aids the design, construction and management of construction projects throughout their lifespan. Globally, the Architecture, Engineering, and Construction (AEC) industry has for decades progressively adopted and implemented BIM. While there are several papers in this context, none have tried to extensively document BIM’s comprehensive contributions and uses in the construction phase. Therefore, this paper aims to identify BIM’s various contributions and uses in the construction phase and analyze publication trends, co-occurring keywords, contributing authors and countries. A systematic overview approach was used to review published articles on state-of-the-art of BIM in construction, supported by bibliometric network mapping analysis. A total of 409 documents were extracted and analyzed. The study’s findings document BIM’s various uses and contributions to the AEC industry, such as simulation of each stage of the construction process, virtual presentation of the building and site, visualization of progress, management of construction work, enhancement of safety, communication and collaboration, quick generation of reliable and accurate cost estimates, assistance in the fast realization of return on investment (ROI), and serving as a platform that hosts and documents various technological tools used during the construction phase. The bibliometric analysis reveals the most contributing scholars, countries, document sources, trend network mapping of co-occurring keywords, and publication trends. The primary practical implications of this study’s discoveries can be exploited as a basis for further research and to influence the future direction of BIM in the AEC industry. The findings will enhance the wider spread, application and understanding of BIM in the AEC industry, thereby increasing BIM awareness and knowledge globally.
{"title":"Overview of BIM contributions in the construction phase: review and bibliometric analysis","authors":"James Olaonipekun Toyin, M. Mewomo","doi":"10.36680/j.itcon.2023.025","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.025","url":null,"abstract":"Building Information Modelling (BIM) is a revolutionary invention within the construction industry that essentially aids the design, construction and management of construction projects throughout their lifespan. Globally, the Architecture, Engineering, and Construction (AEC) industry has for decades progressively adopted and implemented BIM. While there are several papers in this context, none have tried to extensively document BIM’s comprehensive contributions and uses in the construction phase. Therefore, this paper aims to identify BIM’s various contributions and uses in the construction phase and analyze publication trends, co-occurring keywords, contributing authors and countries. A systematic overview approach was used to review published articles on state-of-the-art of BIM in construction, supported by bibliometric network mapping analysis. A total of 409 documents were extracted and analyzed. The study’s findings document BIM’s various uses and contributions to the AEC industry, such as simulation of each stage of the construction process, virtual presentation of the building and site, visualization of progress, management of construction work, enhancement of safety, communication and collaboration, quick generation of reliable and accurate cost estimates, assistance in the fast realization of return on investment (ROI), and serving as a platform that hosts and documents various technological tools used during the construction phase. The bibliometric analysis reveals the most contributing scholars, countries, document sources, trend network mapping of co-occurring keywords, and publication trends. The primary practical implications of this study’s discoveries can be exploited as a basis for further research and to influence the future direction of BIM in the AEC industry. The findings will enhance the wider spread, application and understanding of BIM in the AEC industry, thereby increasing BIM awareness and knowledge globally.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-25DOI: 10.36680/j.itcon.2023.021
Y. Shinde, Kyeongsuk Lee, Beyza Kiper, Makayla Simpson, Sogand Hasanzadeh
While the advancement of visualization technologies—virtual-reality, augmented-reality, mixed-reality, and extended reality—has long produced opportunities to create more realistic simulated environments to provoke and study natural human behavior, recent interest in applying 360° panoramic visualizations has been increasing across several disciplines due to these technologies’ lower costs, higher presence, and greater immersive-ness. However, the variety of applications of 360° panoramas (both images and videos) is limited in the architecture, engineering, and construction (AEC) domain compared to other domains. This paper systematically presents an in-depth understanding of 360° panorama research trends and reveals the challenges and opportunities for future research in the AEC area. In particular, this systematic review analyzed eighty studies across two decades (2000-2022) to consider 360° panoramas’ application areas, methodologies, potential benefits, challenges, best practices, and future research directions for both AEC and non-AEC domains. Several prevalent application domains in AEC—namely architectural studies, construction education and training, construction visualization and progress monitoring, and cognitive analysis and human behavior in the construction industry—were identified. This paper indicates that 360° panoramas provide a higher sense of presence than conventional simulation methods (e.g., virtual reality). Moreover, pairing 360° panorama technologies with a head-mounted display significantly increases immersion when compared with other display options. Lastly, limitations of 360° panoramas, such as cybersickness and technical properties, are discussed. This paper is expected to shed light on the potential of these state-of-the-art technologies in the AEC domain, which can serve both academia and industry.
{"title":"A Systematic Literature Review on 360° Panoramic Applications in Architecture, Engineering, and Construction (AEC) Industry","authors":"Y. Shinde, Kyeongsuk Lee, Beyza Kiper, Makayla Simpson, Sogand Hasanzadeh","doi":"10.36680/j.itcon.2023.021","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.021","url":null,"abstract":"While the advancement of visualization technologies—virtual-reality, augmented-reality, mixed-reality, and extended reality—has long produced opportunities to create more realistic simulated environments to provoke and study natural human behavior, recent interest in applying 360° panoramic visualizations has been increasing across several disciplines due to these technologies’ lower costs, higher presence, and greater immersive-ness. However, the variety of applications of 360° panoramas (both images and videos) is limited in the architecture, engineering, and construction (AEC) domain compared to other domains. This paper systematically presents an in-depth understanding of 360° panorama research trends and reveals the challenges and opportunities for future research in the AEC area. In particular, this systematic review analyzed eighty studies across two decades (2000-2022) to consider 360° panoramas’ application areas, methodologies, potential benefits, challenges, best practices, and future research directions for both AEC and non-AEC domains. Several prevalent application domains in AEC—namely architectural studies, construction education and training, construction visualization and progress monitoring, and cognitive analysis and human behavior in the construction industry—were identified. This paper indicates that 360° panoramas provide a higher sense of presence than conventional simulation methods (e.g., virtual reality). Moreover, pairing 360° panorama technologies with a head-mounted display significantly increases immersion when compared with other display options. Lastly, limitations of 360° panoramas, such as cybersickness and technical properties, are discussed. This paper is expected to shed light on the potential of these state-of-the-art technologies in the AEC domain, which can serve both academia and industry.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-25DOI: 10.36680/j.itcon.2023.022
A. Pavard, A. Dony, Patricia Bordin
The construction sector is undergoing a digital transition. Local authorities have adopted geographic information systems (GISs) to plan their territories and structure their services, such as transport. Simultaneously, building information modelling (BIM) has demonstrated its advantages during the design and construction phases of structures. An infrastructure project can rely on these two technologies to plan its implementation (GIS), to complete its design and construction (BIM), or to manage associated services, such as mobility (GIS). However, road maintenance, an important part of the infrastructure’s life cycle, is not yet covered by these technologies. Road maintenance necessitates a comprehensive view of the infrastructure and its interactions with other real-world objects (e.g. vegetation, technical networks, or vehicles). Moreover, road managers are the local authorities that already use GISs. For these reasons, a GIS is suitable for fulfilling road maintenance requirements. This study presents a spatial framework (GIS) developed for road management. Applying it to a specific case study provides insights on the organisation of the spatial road framework which can be adapted to the infrastructure’s environment management. The spatial dimension must allow for the representation of the road and its components, including pavements and their dependencies. The structural dimension must be detailed to describe the layers, their formulations, and their thicknesses. The condition of the road must be described concisely so that the managers can plan maintenance.
{"title":"Road modelling for infrastructure management - the efficient use of geographic information systems","authors":"A. Pavard, A. Dony, Patricia Bordin","doi":"10.36680/j.itcon.2023.022","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.022","url":null,"abstract":"The construction sector is undergoing a digital transition. Local authorities have adopted geographic information systems (GISs) to plan their territories and structure their services, such as transport. Simultaneously, building information modelling (BIM) has demonstrated its advantages during the design and construction phases of structures. An infrastructure project can rely on these two technologies to plan its implementation (GIS), to complete its design and construction (BIM), or to manage associated services, such as mobility (GIS). However, road maintenance, an important part of the infrastructure’s life cycle, is not yet covered by these technologies. Road maintenance necessitates a comprehensive view of the infrastructure and its interactions with other real-world objects (e.g. vegetation, technical networks, or vehicles). Moreover, road managers are the local authorities that already use GISs. For these reasons, a GIS is suitable for fulfilling road maintenance requirements. This study presents a spatial framework (GIS) developed for road management. Applying it to a specific case study provides insights on the organisation of the spatial road framework which can be adapted to the infrastructure’s environment management. The spatial dimension must allow for the representation of the road and its components, including pavements and their dependencies. The structural dimension must be detailed to describe the layers, their formulations, and their thicknesses. The condition of the road must be described concisely so that the managers can plan maintenance.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-25DOI: 10.36680/j.itcon.2023.023
Aparna Harichandran, B. Raphael, Abhijit Mukherjee
Recognising activities of construction equipment is essential for monitoring productivity, construction progress, safety, and environmental impacts. While there have been many studies on activity recognition of earth excavation and moving equipment, activity identification of Automated Construction Systems (ACS) has been rarely attempted. Especially for low-rise ACS that offers energy-efficient, cost-effective solutions for urgent housing needs, and provides more affordable living options for a broader population. Deep learning methods have gained a lot of attention because of their ability to perform classification without manually extracting relevant features. This study evaluates the feasibility of deep sequence models for developing an activity recognition framework for low-rise automated construction equipment. Time series acceleration data was collected from the structure to identify major operation classes of an ACS. Long Short Term Memory Networks (LSTM) were applied for identifying the activity classes and the performance was compared with that of traditional machine learning classifiers. Diverse augmentation methods were adopted for generating datasets for training the deep learning classifiers. Several recently published literature seem to establish the superiority of complex deep learning techniques over traditional machine learning algorithms regardless of the application context. However, the results of this study show that all the conventional machine learning classifiers perform equivalently or better than deep learning classifiers in identifying activities of the ACS. The performance of the deep learning classifiers is affected by the lack of diversity in the initial dataset. If the augmented dataset significantly alters the characteristics of the original dataset, it may not deliver good recognition results.
{"title":"Relevance of deep sequence models for recognising automated construction activities: a case study on a low-rise construction system","authors":"Aparna Harichandran, B. Raphael, Abhijit Mukherjee","doi":"10.36680/j.itcon.2023.023","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.023","url":null,"abstract":"Recognising activities of construction equipment is essential for monitoring productivity, construction progress, safety, and environmental impacts. While there have been many studies on activity recognition of earth excavation and moving equipment, activity identification of Automated Construction Systems (ACS) has been rarely attempted. Especially for low-rise ACS that offers energy-efficient, cost-effective solutions for urgent housing needs, and provides more affordable living options for a broader population. Deep learning methods have gained a lot of attention because of their ability to perform classification without manually extracting relevant features. This study evaluates the feasibility of deep sequence models for developing an activity recognition framework for low-rise automated construction equipment. Time series acceleration data was collected from the structure to identify major operation classes of an ACS. Long Short Term Memory Networks (LSTM) were applied for identifying the activity classes and the performance was compared with that of traditional machine learning classifiers. Diverse augmentation methods were adopted for generating datasets for training the deep learning classifiers. Several recently published literature seem to establish the superiority of complex deep learning techniques over traditional machine learning algorithms regardless of the application context. However, the results of this study show that all the conventional machine learning classifiers perform equivalently or better than deep learning classifiers in identifying activities of the ACS. The performance of the deep learning classifiers is affected by the lack of diversity in the initial dataset. If the augmented dataset significantly alters the characteristics of the original dataset, it may not deliver good recognition results.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-25DOI: 10.36680/j.itcon.2023.020
Olle Samuelson, Lars Stehn
Digital transformation (DT) is expected to contribute to the construction industry's ability to meet climate and sustainable challenges and increase companies' productivity. This study aims to explore requirements for, and factors affecting DT in the construction industry. This research goes beyond the technology perspective and focus on factors needed to transform the potential of digitalisation to benefits for organisations in the construction industry. A structured literature review is performed where knowledge gaps are identified, and a framework is developed that maps the required changes, as well as the associated challenges, constraints, and implications. The construction industry´s business-to-business logic, and the fragmented and project-based structure is found to have impact on the industry´s development within DT. Mainly regarding the DT aspects disruption, structural changes, organisational barriers, and the central aspect value creation. The understanding of DT by scholars and practitioners in the construction industry is found immature and this calls for further research. The research contributes to understanding of the concept DT and proposes, based on earlier DT literature, an adjusted framework for DT in construction, and points out key areas where research in construction has gaps to fill.
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Pub Date : 2023-07-14DOI: 10.36680/j.itcon.2023.019
G. Paskaleva, A. Mazak-Huemer, Marlène Villeneuve, Johannes Waldhart
The development of software tools is a collaborative process involving both the domain experts and the software engineers. This requires efficient communication considering different expertise and perspectives. Additionally, the two groups utilize language and communication tools in disparate ways. This, in turn, may lead to hidden misunderstandings in the requirement analysis phase and potentially result in implementation problems later on, that is difficult and costly to correct. In this paper, we demonstrate the above mentioned challenge via a use case from the tunneling domain. In particular, during the requirement analysis phase for a software capable of handling the data model of the subsoil. The domain experts in the field can best express the complexity of their domain by describing its artifacts, which in most cases are incomprehensible to the software engineers. We outline a method that interleaves requirement analysis and software modeling to enable an iterative increase of the accuracy and completeness of the information extracted from those artifacts and integrated into a flexible software model, which can produce testable software code automatically. Furthermore, we present a prototypical implementation of our method and a preliminary evaluation of the approach.
{"title":"Automated translation from domain knowledge to software model: EXCEL2UML in the tunneling domain","authors":"G. Paskaleva, A. Mazak-Huemer, Marlène Villeneuve, Johannes Waldhart","doi":"10.36680/j.itcon.2023.019","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.019","url":null,"abstract":"The development of software tools is a collaborative process involving both the domain experts and the software engineers. This requires efficient communication considering different expertise and perspectives. Additionally, the two groups utilize language and communication tools in disparate ways. This, in turn, may lead to hidden misunderstandings in the requirement analysis phase and potentially result in implementation problems later on, that is difficult and costly to correct. In this paper, we demonstrate the above mentioned challenge via a use case from the tunneling domain. In particular, during the requirement analysis phase for a software capable of handling the data model of the subsoil. The domain experts in the field can best express the complexity of their domain by describing its artifacts, which in most cases are incomprehensible to the software engineers. We outline a method that interleaves requirement analysis and software modeling to enable an iterative increase of the accuracy and completeness of the information extracted from those artifacts and integrated into a flexible software model, which can produce testable software code automatically. Furthermore, we present a prototypical implementation of our method and a preliminary evaluation of the approach.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-14DOI: 10.36680/j.itcon.2023.018
Yueren Wang, I. Flood
The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems.
{"title":"Machine learning approaches to determining truck type from bridge loading response","authors":"Yueren Wang, I. Flood","doi":"10.36680/j.itcon.2023.018","DOIUrl":"https://doi.org/10.36680/j.itcon.2023.018","url":null,"abstract":"The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69721637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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