Pub Date : 2024-08-01DOI: 10.1088/1755-1315/1390/1/012019
L R Asfandiyarova, G V Khakimova, I V Ovsyannikova
Currently, the oil and gas industry remains the leader in meeting the global need for energy resources. The high intensity of hydrocarbon production naturally leads to an increase in the volume of petrochemical waste that ends up in the natural environment. In this regard, the implementation of toxicological assessment of oil-containing soils and water bodies is one of the most important environmental tasks. Biotesting allows for a quick integral assessment of the properties of a contaminated environment. This method of toxicity analysis is economical and does not require the use of special chemical reagents and equipment. The seeds of higher vegetation are the most accessible, easy to use and universal test objects. However, it is necessary to use only species that are highly sensitive to pollutants. This article is based on the results of a study of the phytotoxicity of gray forest soil contaminated with petroleum products using wheat. Experiments have shown that soil contaminated with oil waste has a strong inhibitory effect on wheat seedlings. It was found that small doses of petroleum products (0.31% oil) have a stimulating effect on seed germination in the analyzed soil.
{"title":"Wheat as a test object for determining the degree of soil contamination with petroleum products","authors":"L R Asfandiyarova, G V Khakimova, I V Ovsyannikova","doi":"10.1088/1755-1315/1390/1/012019","DOIUrl":"https://doi.org/10.1088/1755-1315/1390/1/012019","url":null,"abstract":"Currently, the oil and gas industry remains the leader in meeting the global need for energy resources. The high intensity of hydrocarbon production naturally leads to an increase in the volume of petrochemical waste that ends up in the natural environment. In this regard, the implementation of toxicological assessment of oil-containing soils and water bodies is one of the most important environmental tasks. Biotesting allows for a quick integral assessment of the properties of a contaminated environment. This method of toxicity analysis is economical and does not require the use of special chemical reagents and equipment. The seeds of higher vegetation are the most accessible, easy to use and universal test objects. However, it is necessary to use only species that are highly sensitive to pollutants. This article is based on the results of a study of the phytotoxicity of gray forest soil contaminated with petroleum products using wheat. Experiments have shown that soil contaminated with oil waste has a strong inhibitory effect on wheat seedlings. It was found that small doses of petroleum products (0.31% oil) have a stimulating effect on seed germination in the analyzed soil.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200534","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012010
Kristine Hjemgård
This study explores the potential of machine learning to predict the risk of accidents in construction projects. Data has been gathered from a Norwegian construction company over a period of nearly seven years, consisting of 156 projects. 46 features are constructed, primarily focusing on observations and incidents on health, safety, and environment, as well as quality deviations. Using mutual information, 20 important features are identified. These are later used to train six classification models, which are evaluated using 10-fold cross-validation. The target feature of the classification problem is the level of risk, which describes the probability of accidents for a project: low risk, risk of less severe accidents, risk of serious accidents, and risk of critical accidents. The model performances are poor compared to previous studies. This is likely a result of the amount of projects and the total number of different features used to train the models. Based on the limited data that is utilized, the results still indicate that there is a potential in some of the data, especially observations and incidents. It is suggested that incorporating project worker-related data and more project information could enhance the accuracy of predictions.
{"title":"Prediction of accident risk in construction projects using data on safety and quality deviations from a Norwegian company","authors":"Kristine Hjemgård","doi":"10.1088/1755-1315/1389/1/012010","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012010","url":null,"abstract":"This study explores the potential of machine learning to predict the risk of accidents in construction projects. Data has been gathered from a Norwegian construction company over a period of nearly seven years, consisting of 156 projects. 46 features are constructed, primarily focusing on observations and incidents on health, safety, and environment, as well as quality deviations. Using mutual information, 20 important features are identified. These are later used to train six classification models, which are evaluated using 10-fold cross-validation. The target feature of the classification problem is the level of risk, which describes the probability of accidents for a project: low risk, risk of less severe accidents, risk of serious accidents, and risk of critical accidents. The model performances are poor compared to previous studies. This is likely a result of the amount of projects and the total number of different features used to train the models. Based on the limited data that is utilized, the results still indicate that there is a potential in some of the data, especially observations and incidents. It is suggested that incorporating project worker-related data and more project information could enhance the accuracy of predictions.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200522","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012009
Hisham Abou-Ibrahim, Eelon Lappalainen, Jyrki Oraskari, Atoosa Aliheidarloo
The requirements of modern buildings have increased the complexity of design, where several systems need to be developed and coordinated simultaneously. Although BIM has improved the construction process, it has also led to increased information size generated by each discipline, which complicates the monitoring and control of the process. Several tools allow the one-to-one comparison of BIM model versions to reveal progress aspects related to location, geometry, and property changes. Although these tools are beneficial, current platforms do not document progress over time or reveal the zones in the BIM model where design efforts are exercised. This study develops an artefact that (1) tracks changes in model versions over time, (2) categorises them based on location and recency, and (3) visualises the progress while showing the areas having the main design buzz. The visual artefact is expected to increase process transparency among different disciplines, promote concurrent engineering by carefully managing design works in selected zones, enhance the control of design progress against a pre-set schedule, and support takt production by linking current design work to the information needs of takt areas.
{"title":"Monitoring Design Buzz: A Visual BIM-Based Approach","authors":"Hisham Abou-Ibrahim, Eelon Lappalainen, Jyrki Oraskari, Atoosa Aliheidarloo","doi":"10.1088/1755-1315/1389/1/012009","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012009","url":null,"abstract":"The requirements of modern buildings have increased the complexity of design, where several systems need to be developed and coordinated simultaneously. Although BIM has improved the construction process, it has also led to increased information size generated by each discipline, which complicates the monitoring and control of the process. Several tools allow the one-to-one comparison of BIM model versions to reveal progress aspects related to location, geometry, and property changes. Although these tools are beneficial, current platforms do not document progress over time or reveal the zones in the BIM model where design efforts are exercised. This study develops an artefact that (1) tracks changes in model versions over time, (2) categorises them based on location and recency, and (3) visualises the progress while showing the areas having the main design buzz. The visual artefact is expected to increase process transparency among different disciplines, promote concurrent engineering by carefully managing design works in selected zones, enhance the control of design progress against a pre-set schedule, and support takt production by linking current design work to the information needs of takt areas.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200524","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012040
Marit Støre-Valen
Re-use of building materials and -components (BMC) in renovation and transformation project has a great potential to capture Circular Economy principles (CE). This study looks at strategies and sustainable development goals among a selection of public and private building owners and how they are practising circular principles in Scandinavian renovation and transformation projects. This is done by studying national and international research literature, national and European (EU) policy documents, business strategy documents and semi-structured in-depth interviews among private and public building owners. In Scandinavia, there are national strategies and regulations that promote circularity within the building sector. However, on a local level, only the large city municipalities have applied this by local guidelines and action plans. The research literature and the building owners interviewed, points towards the need to collaborate to reach national SDG’s. One suggestion is that the building material suppliers take responsibility to store and resell BMC, ensuring quality documentation. The building owners report that the main motivation to do re-use of BMC in renovation projects is the focus on the climate gas reduction, competence development and the internal environmental goals. However, further collaboration, competence and innovation is needed to upscale re-use of BMC to a larger market.
{"title":"Strategies of re-use of building materials and components (BMC) in Scandinavian renovation and transformation projects","authors":"Marit Støre-Valen","doi":"10.1088/1755-1315/1389/1/012040","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012040","url":null,"abstract":"Re-use of building materials and -components (BMC) in renovation and transformation project has a great potential to capture Circular Economy principles (CE). This study looks at strategies and sustainable development goals among a selection of public and private building owners and how they are practising circular principles in Scandinavian renovation and transformation projects. This is done by studying national and international research literature, national and European (EU) policy documents, business strategy documents and semi-structured in-depth interviews among private and public building owners. In Scandinavia, there are national strategies and regulations that promote circularity within the building sector. However, on a local level, only the large city municipalities have applied this by local guidelines and action plans. The research literature and the building owners interviewed, points towards the need to collaborate to reach national SDG’s. One suggestion is that the building material suppliers take responsibility to store and resell BMC, ensuring quality documentation. The building owners report that the main motivation to do re-use of BMC in renovation projects is the focus on the climate gas reduction, competence development and the internal environmental goals. However, further collaboration, competence and innovation is needed to upscale re-use of BMC to a larger market.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200556","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012042
Rodrigo Pedral Sampaio, António Aguiar Costa, Inês Flores-Colen, Nora Johanne Klungseth, Marco Giovanni Semini, Sondre Nordvik
The architecture, engineering, construction, and facility management (AECFM) industry has been criticized for having a level of digitalization far below that of other manufacturing sectors, especially in creating digital assets, expanding digital tools, and creating digital jobs. The challenge of managing the built environment is currently particularly complex, considering the increasing performance requirements not only in terms of energy and the environment but also from a human perspective. Digital twin technology, with its ability to create virtual replicas of physical assets, has gained significant traction in the built environment sector. These virtual twins offer immense potential for monitoring, simulation, and optimization. Nonetheless, to fully harness their benefits, an integrated framework is essential. Such a framework would guide seamless implementation, interoperability, and effective utilization of digital twins across various domains. Several questions may arise when we create a digital twin for the built environment: How should we structure the digital twin model? What information should we be able to visualize? How should the digital twin model interact with the user? These are some of the questions we still need a consistent answer. A mixed-methods approach that combined a literature review with with expert interviews was used to understand better the current state of digital twin applications in the built environment sector. This study emphasizes the significance of using an integrated approach to develop digital twin technology to realize its full potential in the built environment industry. It puts forward a framework specifically designed for hospital facilities.
{"title":"An Integrated Framework for Digital Twins in Hospitals","authors":"Rodrigo Pedral Sampaio, António Aguiar Costa, Inês Flores-Colen, Nora Johanne Klungseth, Marco Giovanni Semini, Sondre Nordvik","doi":"10.1088/1755-1315/1389/1/012042","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012042","url":null,"abstract":"The architecture, engineering, construction, and facility management (AECFM) industry has been criticized for having a level of digitalization far below that of other manufacturing sectors, especially in creating digital assets, expanding digital tools, and creating digital jobs. The challenge of managing the built environment is currently particularly complex, considering the increasing performance requirements not only in terms of energy and the environment but also from a human perspective. Digital twin technology, with its ability to create virtual replicas of physical assets, has gained significant traction in the built environment sector. These virtual twins offer immense potential for monitoring, simulation, and optimization. Nonetheless, to fully harness their benefits, an integrated framework is essential. Such a framework would guide seamless implementation, interoperability, and effective utilization of digital twins across various domains. Several questions may arise when we create a digital twin for the built environment: How should we structure the digital twin model? What information should we be able to visualize? How should the digital twin model interact with the user? These are some of the questions we still need a consistent answer. A mixed-methods approach that combined a literature review with with expert interviews was used to understand better the current state of digital twin applications in the built environment sector. This study emphasizes the significance of using an integrated approach to develop digital twin technology to realize its full potential in the built environment industry. It puts forward a framework specifically designed for hospital facilities.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200595","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}
The world is suffering from increasing weather extremes caused by climate change of which the building sector is a major contributor. There is however a large reduction potential in the sector and circular economy has received increased attention both within research and practice. This study explores circular futures within the building sector through the futures studies method of backcasting. Two circular futures for the year 2035 are imagined in separate expert workshops. In one workshop the future described is one where buildings are only extended vertically and no new construction takes place, and the other workshop describes one where vacant office buildings are adapted to housing. The aim is to establish themes which may guide the building sector to become more circular through building adaptation. Many themes are common for both futures, such as the urgency of change and the importance of political willingness and action. Further, social acceptance, funding, and economic feasibility assessments emerged as important. Working with existing buildings undoubtedly entails higher uncertainty than new construction. Tools to adequately account for this uncertainty, without the need to exaggerate the risk in lifecycle costing, could improve the uptake of both adaptive reuse and vertical extension projects. The findings contribute to new knowledge of themes to enable a more circular built environment, and are useful for researchers, practitioners and policymakers wanting to contribute to a more circular real estate and construction sector.
{"title":"Urgency to action: Enabling circular futures for the building sector","authors":"Rebecka Lundgren, Lassi Tähtinen, Riikka Kyrö, Saija Toivonen","doi":"10.1088/1755-1315/1389/1/012003","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012003","url":null,"abstract":"The world is suffering from increasing weather extremes caused by climate change of which the building sector is a major contributor. There is however a large reduction potential in the sector and circular economy has received increased attention both within research and practice. This study explores circular futures within the building sector through the futures studies method of backcasting. Two circular futures for the year 2035 are imagined in separate expert workshops. In one workshop the future described is one where buildings are only extended vertically and no new construction takes place, and the other workshop describes one where vacant office buildings are adapted to housing. The aim is to establish themes which may guide the building sector to become more circular through building adaptation. Many themes are common for both futures, such as the urgency of change and the importance of political willingness and action. Further, social acceptance, funding, and economic feasibility assessments emerged as important. Working with existing buildings undoubtedly entails higher uncertainty than new construction. Tools to adequately account for this uncertainty, without the need to exaggerate the risk in lifecycle costing, could improve the uptake of both adaptive reuse and vertical extension projects. The findings contribute to new knowledge of themes to enable a more circular built environment, and are useful for researchers, practitioners and policymakers wanting to contribute to a more circular real estate and construction sector.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200521","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012004
Arianna Minoretti, Agnar Johansen, Paulos Wondimu
Sustainable transport is one of the strategic goals of public roads administrations. The Norwegian Public Roads Administration is working to develop a sustainable portfolio of projects. Literature on sustainable portfolio management for the infrastructure sector could help in developing strategies for portfolio management and succeed the challenges. The purpose of the paper is to identify existing literature on sustainable portfolio management in the transport sector and identify key findings in the existing literature. A literature review is performed by combining the three main keywords of “portfolio management”, “sustainability” and “infrastructure”. Structured search is performed in scientific databases, such as Google Scholar, Scopus and Web of Science, considering relevant keywords’ synonyms and using string combinations. The study shows that there is scarce relevant literature dealing with the topic of interest, combining all the chosen keywords. Partial combinations of the keywords are investigated. The results provide five key findings on the paper’s topic. First, the existing literature on Portfolio Management focused on sustainability is more on energy, finance, data, or product fields, and is seldom related to infrastructure. Second, the literature focuses more on project portfolio selection than on portfolio management. Third, when the literature focuses on constructions, different sets of criteria are used to identify sustainability. As a result, sustainability does not have a unique definition. Fourth, there is little available literature on tools and methodologies for using a set of criteria to select projects specific to the transport sector. Fifth, there is no unison agreement in the literature on which tools and methodologies to use for Portfolio Management in the infrastructure sector. In conclusion, the paper identifies a gap in Portfolio Management focused on sustainability in the infrastructure sector.
可持续交通是公共道路管理部门的战略目标之一。挪威公共道路管理局正在努力开发可持续的项目组合。有关基础设施部门可持续项目组合管理的文献可帮助制定项目组合管理战略并应对挑战。本文旨在确定有关运输部门可持续项目组合管理的现有文献，并确定现有文献中的主要结论。文献综述结合了 "组合管理"、"可持续性 "和 "基础设施 "三个主要关键词。在 Google Scholar、Scopus 和 Web of Science 等科学数据库中进行了结构化搜索，考虑了相关关键词的同义词并使用了字符串组合。研究表明，结合所有选定的关键词，涉及相关主题的相关文献很少。对关键词的部分组合进行了研究。研究结果提供了有关论文主题的五项重要发现。首先，现有的以可持续发展为重点的投资组合管理文献多涉及能源、金融、数据或产品领域，很少与基础设施相关。其次，文献更多地关注项目组合选择而非组合管理。第三，当文献关注建筑时，会使用不同的标准来确定可持续性。因此，可持续性并没有一个独特的定义。第四，关于使用一套标准来选择运输部门特定项目的工具和方法的文献很少。第五，关于在基础设施部门使用哪些工具和方法进行项目组合管理，文献中没有统一的意见。总之，本文指出了以基础设施部门可持续性为重点的项目组合管理方面的差距。
{"title":"Sustainability in project portfolios: a scoping literature review for the transport sector","authors":"Arianna Minoretti, Agnar Johansen, Paulos Wondimu","doi":"10.1088/1755-1315/1389/1/012004","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012004","url":null,"abstract":"Sustainable transport is one of the strategic goals of public roads administrations. The Norwegian Public Roads Administration is working to develop a sustainable portfolio of projects. Literature on sustainable portfolio management for the infrastructure sector could help in developing strategies for portfolio management and succeed the challenges. The purpose of the paper is to identify existing literature on sustainable portfolio management in the transport sector and identify key findings in the existing literature. A literature review is performed by combining the three main keywords of “portfolio management”, “sustainability” and “infrastructure”. Structured search is performed in scientific databases, such as Google Scholar, Scopus and Web of Science, considering relevant keywords’ synonyms and using string combinations. The study shows that there is scarce relevant literature dealing with the topic of interest, combining all the chosen keywords. Partial combinations of the keywords are investigated. The results provide five key findings on the paper’s topic. First, the existing literature on Portfolio Management focused on sustainability is more on energy, finance, data, or product fields, and is seldom related to infrastructure. Second, the literature focuses more on project portfolio selection than on portfolio management. Third, when the literature focuses on constructions, different sets of criteria are used to identify sustainability. As a result, sustainability does not have a unique definition. Fourth, there is little available literature on tools and methodologies for using a set of criteria to select projects specific to the transport sector. Fifth, there is no unison agreement in the literature on which tools and methodologies to use for Portfolio Management in the infrastructure sector. In conclusion, the paper identifies a gap in Portfolio Management focused on sustainability in the infrastructure sector.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200526","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012038
Mirza Muntasir Nishat, Sander Magnussen Neraas, Andrei Marsov, Nils O.E. Olsson
Project activity delays caused by variation orders (VOs) can compromise the achievement of timely project completion. Previous research on machine learning (ML) applications for delay predictions has mainly been concerned with delays on a whole project level, whereas predictions of delays in individual project activities have received less attention. This study is a pilot study to investigate how data from large project databases can be used for an ML analysis. The application is aimed at providing early warnings of delays related to VOs in construction projects. The study was performed following typical ML model development steps including data collection, data preprocessing, model training, and testing. A compound dataset was retrieved from project-planning software utilised in a large project. Four pilot tree-based ML models, namely, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were trained and tested on a pre-processed dataset comprising 11194 activities. The overall best-performing model was Random Forest with 92.7% and 91.8% recall on DELAYED START and DELAYED FINISH, respectively. By emphasizing that project participants’ competency and personal accountability might influence the timely implementation of scope adjustments, these findings advance the field of project management research. An approach like the use of tree-based ML algorithms is applicable for analyses of individual activities in other construction projects. Considering the capability of ML algorithms to capture complex interconnections in raw data extracted from project-planning software, further development of such ML models will enable the establishment of an AI-based Early Warning System (EWS) that can flag potential delays caused by VO requests.
变更单（VOs）导致的项目活动延迟会影响项目的按时完成。以往有关机器学习（ML）应用于延迟预测的研究主要涉及整个项目层面的延迟，而对单个项目活动延迟的预测则关注较少。本研究是一项试点研究，旨在探讨如何将大型项目数据库中的数据用于 ML 分析。应用的目的是对建筑项目中与 VO 相关的延误提供预警。研究按照典型的 ML 模型开发步骤进行，包括数据收集、数据预处理、模型训练和测试。从一个大型项目中使用的项目规划软件中获取了一个复合数据集。在由 11194 个活动组成的预处理数据集上训练和测试了四个基于树的 ML 模型，即决策树、随机森林、AdaBoost 和梯度提升。总体表现最佳的模型是随机森林，在延迟开始和延迟结束方面的召回率分别为 92.7% 和 91.8%。通过强调项目参与者的能力和个人责任可能会影响范围调整的及时实施，这些发现推动了项目管理研究领域的发展。像使用基于树的 ML 算法这样的方法也适用于对其他建筑项目中的单个活动进行分析。考虑到 ML 算法能够捕捉从项目规划软件中提取的原始数据中复杂的相互联系，进一步开发此类 ML 模型将有助于建立一个基于人工智能的预警系统（EWS），该系统可标记出 VO 请求可能造成的延误。
{"title":"Prediction of project activity delays caused by variation orders: a machine-learning approach","authors":"Mirza Muntasir Nishat, Sander Magnussen Neraas, Andrei Marsov, Nils O.E. Olsson","doi":"10.1088/1755-1315/1389/1/012038","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012038","url":null,"abstract":"Project activity delays caused by variation orders (VOs) can compromise the achievement of timely project completion. Previous research on machine learning (ML) applications for delay predictions has mainly been concerned with delays on a whole project level, whereas predictions of delays in individual project activities have received less attention. This study is a pilot study to investigate how data from large project databases can be used for an ML analysis. The application is aimed at providing early warnings of delays related to VOs in construction projects. The study was performed following typical ML model development steps including data collection, data preprocessing, model training, and testing. A compound dataset was retrieved from project-planning software utilised in a large project. Four pilot tree-based ML models, namely, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were trained and tested on a pre-processed dataset comprising 11194 activities. The overall best-performing model was Random Forest with 92.7% and 91.8% recall on DELAYED START and DELAYED FINISH, respectively. By emphasizing that project participants’ competency and personal accountability might influence the timely implementation of scope adjustments, these findings advance the field of project management research. An approach like the use of tree-based ML algorithms is applicable for analyses of individual activities in other construction projects. Considering the capability of ML algorithms to capture complex interconnections in raw data extracted from project-planning software, further development of such ML models will enable the establishment of an AI-based Early Warning System (EWS) that can flag potential delays caused by VO requests.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200571","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012013
A Ainamo, A Peltokorpi
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are technologies that have recently transformed many industries. The construction industry has traditionally been a laggard industry in terms of digital-technology adoption. When leading firms in this industry have experimented with these technologies, many of these experiments have met resistance. In this paper we take an institutional lens to study why and particular social structures appears to have contributed to the resistance and paucity of success stories. Within institutional research, we focus on research with traces to cognitive science and psychology. We have carried out a qualitative embedded multiple-case study on resistance to new technologies and how to overcome such resistance. The study involves four use cases in the Finnish construction industry: (1) automation of a material-product subcontractor’s production planning; (2) business-model innovation by contractor on how to best work across multiple construction sites at once; (3) machine learning and automation of documentation by a software firm; and (4) promotion of a vision of information sharing across organizations by the above software firm. Based on within and cross-case analyses, preliminary empirical findings are that AI, ML and DL have in the Finnish construction industry challenged institutionalized forms of organizing and workflow established long since in the industry and, until about the time of this piece of research, taken for granted. Resistance was nonetheless beginning to be overcome at the time of writing this piece of research with small-group interaction across firms – such as those in this study - - in the industry ecosystem. Human-human mediation and face-to-face encounters were building trust in and across the organizations. The implication for practice and policy is that business transformation will not quickly and autonomously transform into “impersonal” or machine-machine exchange but, before that, requires human-human mediation. “ In the long-term, AI and analytics have boundless potential use cases in E&C [i.e. engineering and construction]. Machine learning is gaining some momentum as an overarching use case (that is, one applicable to the entire construction life cycle, from preconstruction through O&M 8i.e. operations and management), particularly in reality capture (for example, in conjunction with computer vision) as well as for comparison of in situ field conditions with plans (for example, supporting twin models). Indeed, by applying machine learning to an ongoing project, schedules could be optimized to sequence tasks and hit target deadlines, and divergences from blueprints could be caught closer to real time and corrected using a variety of predetermined potential scenarios.” [1]
{"title":"Innovation meets institutions: AI and the Finnish construction ecosystem","authors":"A Ainamo, A Peltokorpi","doi":"10.1088/1755-1315/1389/1/012013","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012013","url":null,"abstract":"Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are technologies that have recently transformed many industries. The construction industry has traditionally been a laggard industry in terms of digital-technology adoption. When leading firms in this industry have experimented with these technologies, many of these experiments have met resistance. In this paper we take an institutional lens to study why and particular social structures appears to have contributed to the resistance and paucity of success stories. Within institutional research, we focus on research with traces to cognitive science and psychology. We have carried out a qualitative embedded multiple-case study on resistance to new technologies and how to overcome such resistance. The study involves four use cases in the Finnish construction industry: (1) automation of a material-product subcontractor’s production planning; (2) business-model innovation by contractor on how to best work across multiple construction sites at once; (3) machine learning and automation of documentation by a software firm; and (4) promotion of a vision of information sharing across organizations by the above software firm. Based on within and cross-case analyses, preliminary empirical findings are that AI, ML and DL have in the Finnish construction industry challenged institutionalized forms of organizing and workflow established long since in the industry and, until about the time of this piece of research, taken for granted. Resistance was nonetheless beginning to be overcome at the time of writing this piece of research with small-group interaction across firms – such as those in this study - - in the industry ecosystem. Human-human mediation and face-to-face encounters were building trust in and across the organizations. The implication for practice and policy is that business transformation will not quickly and autonomously transform into “impersonal” or machine-machine exchange but, before that, requires human-human mediation. <italic toggle=\"yes\">“ In the long-term, AI and analytics have boundless potential use cases in E&C</italic> [i.e. engineering and construction]<italic toggle=\"yes\">. Machine learning is gaining some momentum as an overarching use case (that is, one applicable to the entire construction life cycle, from preconstruction through O&M 8i.e.</italic> operations and management<italic toggle=\"yes\">), particularly in reality capture (for example, in conjunction with computer vision) as well as for comparison of in situ field conditions with plans (for example, supporting twin models). Indeed, by applying machine learning to an ongoing project, schedules could be optimized to sequence tasks and hit target deadlines, and divergences from blueprints could be caught closer to real time and corrected using a variety of predetermined potential scenarios.”</italic> [1]","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200555","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 : 2024-08-01DOI: 10.1088/1755-1315/1389/1/012039
S. Wandahl, C. T. Pérez, S. T. Salling
The construction industry is labor-intensive, where project performance relies on a successful collaboration between construction workers between workers and managers. Hence, knowledge about workers’ job satisfaction is highly valuable for improving the efficiency of the construction industry. For that reason, a survey was conducted to understand construction workers’ perceptions of factors that influence job satisfaction. The questionnaire included 12 demographic questions and 27 questions. Data was collected in 2023, and almost 3400 workers answered the questionnaire. Several relevant insights surfaced during the data analysis. Among others, the more the workers are engaged and involved in planning, the higher the performance and job satisfaction. The more digital tools are used, the better performance and job satisfaction. However, workers find that digital tools are used too little. The younger workforce is more troubled than the older.
{"title":"Construction Workers’ Perception of Project Management, Work Environment, and Health & Safety","authors":"S. Wandahl, C. T. Pérez, S. T. Salling","doi":"10.1088/1755-1315/1389/1/012039","DOIUrl":"https://doi.org/10.1088/1755-1315/1389/1/012039","url":null,"abstract":"The construction industry is labor-intensive, where project performance relies on a successful collaboration between construction workers between workers and managers. Hence, knowledge about workers’ job satisfaction is highly valuable for improving the efficiency of the construction industry. For that reason, a survey was conducted to understand construction workers’ perceptions of factors that influence job satisfaction. The questionnaire included 12 demographic questions and 27 questions. Data was collected in 2023, and almost 3400 workers answered the questionnaire. Several relevant insights surfaced during the data analysis. Among others, the more the workers are engaged and involved in planning, the higher the performance and job satisfaction. The more digital tools are used, the better performance and job satisfaction. However, workers find that digital tools are used too little. The younger workforce is more troubled than the older.","PeriodicalId":14556,"journal":{"name":"IOP Conference Series: Earth and Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225668","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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