Purpose This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model. Design/methodology/approach This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images. Findings The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects. Originality/value This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.
{"title":"Synthetic images generation for semantic understanding in facility management","authors":"Luca Rampini, F. Re Cecconi","doi":"10.1108/ci-09-2022-0232","DOIUrl":"https://doi.org/10.1108/ci-09-2022-0232","url":null,"abstract":"\u0000Purpose\u0000This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.\u0000\u0000\u0000Design/methodology/approach\u0000This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.\u0000\u0000\u0000Findings\u0000The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.\u0000\u0000\u0000Originality/value\u0000This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41363473","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}
Nadia Safura Zabidin, S. Belayutham, C. K. C. Che Ibrahim
Purpose The purpose of this study is to explore the knowledge, attitude and practices (KAP) of Industry 4.0 between the academicians and industry players in construction engineering, further suggesting a mechanism to narrow the gap between the distinct parties. Design/methodology/approach This study was conducted through structured online and face-to-face interviews, using KAP survey, and semi-structured interviews. This constructive research was conducted among Malaysian construction industry players and academicians from the construction engineering department in public universities. Findings The findings exhibit the similarities and differences of KAP between academics and industry on Industry 4.0 in construction engineering. In general, both categories of respondents have displayed more similarities than differences in all aspects, except for knowledge. The better knowledge profile of Industry 4.0 among the academicians reflects the nature of the academic works that constantly seek new knowledge, thus suggesting the establishment of an industry-academic (I-A) knowledge equilibrium framework to leverage the knowledge profile between both parties. Research limitations/implications This exploratory study that showcases the perspective of the academia and industry practitioners on Industry 4.0 acts as a cornerstone for bridging the gap between the two distinct sectors within the same field. Practical implications The gap between the academic and industry was highlighted, further establishing the I-A knowledge equilibrium framework that could also be applied to other fields of study. Originality/value The originality of this paper was the profiling of the KAP of Industry 4.0 for the academicians and industry players in construction engineering, further distinguishing the gap between both parties.
{"title":"The knowledge, attitude and practices (KAP) of Industry 4.0 between construction practitioners and academicians in Malaysia: a comparative study","authors":"Nadia Safura Zabidin, S. Belayutham, C. K. C. Che Ibrahim","doi":"10.1108/ci-05-2022-0109","DOIUrl":"https://doi.org/10.1108/ci-05-2022-0109","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to explore the knowledge, attitude and practices (KAP) of Industry 4.0 between the academicians and industry players in construction engineering, further suggesting a mechanism to narrow the gap between the distinct parties.\u0000\u0000\u0000Design/methodology/approach\u0000This study was conducted through structured online and face-to-face interviews, using KAP survey, and semi-structured interviews. This constructive research was conducted among Malaysian construction industry players and academicians from the construction engineering department in public universities.\u0000\u0000\u0000Findings\u0000The findings exhibit the similarities and differences of KAP between academics and industry on Industry 4.0 in construction engineering. In general, both categories of respondents have displayed more similarities than differences in all aspects, except for knowledge. The better knowledge profile of Industry 4.0 among the academicians reflects the nature of the academic works that constantly seek new knowledge, thus suggesting the establishment of an industry-academic (I-A) knowledge equilibrium framework to leverage the knowledge profile between both parties.\u0000\u0000\u0000Research limitations/implications\u0000This exploratory study that showcases the perspective of the academia and industry practitioners on Industry 4.0 acts as a cornerstone for bridging the gap between the two distinct sectors within the same field.\u0000\u0000\u0000Practical implications\u0000The gap between the academic and industry was highlighted, further establishing the I-A knowledge equilibrium framework that could also be applied to other fields of study.\u0000\u0000\u0000Originality/value\u0000The originality of this paper was the profiling of the KAP of Industry 4.0 for the academicians and industry players in construction engineering, further distinguishing the gap between both parties.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45532030","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}
Purpose This study aims to use social media data mining to revitalize and support existing urban infrastructure monitoring strategies by extracting valuable insights from public opinion, as current strategies struggle with issues such as adaptability to changing conditions, public engagement and cost effectiveness. Design/methodology/approach Twitter messages or “Tweets” about public infrastructure in the Philippines were gathered and analyzed to discover reoccurring concerns in public infrastructure, emerging topics in public debates and the people’s general view of infrastructure services. Findings This study proposes a topic model for extracting dominating subjects from aggregated social media data, as well as a sentiment analysis model for determining public opinion sentiment toward various urban infrastructure components. Originality/value The findings of this study highlight the potential of social media data mining to go beyond the limitations of traditional data collection techniques, as well as the importance of public opinion as a key driver for more user-involved infrastructure management and as an important social aspect that can be used to support planning and response strategies in routine maintenance, preservation and improvement of urban infrastructure systems.
{"title":"Social media sensing framework for urban infrastructure management: a Philippine case study","authors":"S. T. Do, V. Nguyen, Denver Banlasan","doi":"10.1108/ci-04-2022-0082","DOIUrl":"https://doi.org/10.1108/ci-04-2022-0082","url":null,"abstract":"\u0000Purpose\u0000This study aims to use social media data mining to revitalize and support existing urban infrastructure monitoring strategies by extracting valuable insights from public opinion, as current strategies struggle with issues such as adaptability to changing conditions, public engagement and cost effectiveness.\u0000\u0000\u0000Design/methodology/approach\u0000Twitter messages or “Tweets” about public infrastructure in the Philippines were gathered and analyzed to discover reoccurring concerns in public infrastructure, emerging topics in public debates and the people’s general view of infrastructure services.\u0000\u0000\u0000Findings\u0000This study proposes a topic model for extracting dominating subjects from aggregated social media data, as well as a sentiment analysis model for determining public opinion sentiment toward various urban infrastructure components.\u0000\u0000\u0000Originality/value\u0000The findings of this study highlight the potential of social media data mining to go beyond the limitations of traditional data collection techniques, as well as the importance of public opinion as a key driver for more user-involved infrastructure management and as an important social aspect that can be used to support planning and response strategies in routine maintenance, preservation and improvement of urban infrastructure systems.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49130110","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}
F. Parisi, V. Sangiorgio, N. Parisi, A. M. Mangini, M. P. Fanti, J. Adam
Purpose Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process. Design/methodology/approach The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance. Findings The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization. Originality/value In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent. Practical implications The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.
{"title":"A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning","authors":"F. Parisi, V. Sangiorgio, N. Parisi, A. M. Mangini, M. P. Fanti, J. Adam","doi":"10.1108/ci-10-2022-0278","DOIUrl":"https://doi.org/10.1108/ci-10-2022-0278","url":null,"abstract":"\u0000Purpose\u0000Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.\u0000\u0000\u0000Design/methodology/approach\u0000The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.\u0000\u0000\u0000Findings\u0000The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.\u0000\u0000\u0000Originality/value\u0000In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.\u0000\u0000\u0000Practical implications\u0000The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49465770","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}
A. R. Radzi, N. Azmi, S. Kamaruzzaman, R. Rahman, E. Papadonikolaki
Purpose Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM. Design/methodology/approach A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed. Findings The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes. Originality/value This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.
{"title":"Relationship between digital twin and building information modeling: a systematic review and future directions","authors":"A. R. Radzi, N. Azmi, S. Kamaruzzaman, R. Rahman, E. Papadonikolaki","doi":"10.1108/ci-07-2022-0183","DOIUrl":"https://doi.org/10.1108/ci-07-2022-0183","url":null,"abstract":"\u0000Purpose\u0000Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM.\u0000\u0000\u0000Design/methodology/approach\u0000A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed.\u0000\u0000\u0000Findings\u0000The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes.\u0000\u0000\u0000Originality/value\u0000This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43410280","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}
Purpose Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles. Design/methodology/approach The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method. Findings The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models. Originality/value The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase.
{"title":"Method for automating the processes of generating and using 4D BIM models integrated with location-based planning and Last Planner® System","authors":"B. F. Silveira, D. Costa","doi":"10.1108/ci-02-2022-0030","DOIUrl":"https://doi.org/10.1108/ci-02-2022-0030","url":null,"abstract":"\u0000Purpose\u0000Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles.\u0000\u0000\u0000Design/methodology/approach\u0000The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method.\u0000\u0000\u0000Findings\u0000The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models.\u0000\u0000\u0000Originality/value\u0000The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48103037","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}
Ayodeji Emmanuel Oke, John Aliu, P. Tunji-Olayeni, T. Abayomi
Purpose This paper aims to identify and evaluate the challenges affecting the adoption of gamification practices in developing countries through the lens of the Nigerian construction industry. Design/methodology/approach A scoping literature review was conducted through which challenges to the adoption of gamification practices were identified, which helped in the formulation of a questionnaire survey. Data was obtained from construction professionals including architects, builders, engineers and quantity surveyors. Retrieved data were analyzed using several statistical tools such as percentages, frequencies, mean item scores (MIS) and exploratory factor analyses. Findings Based on the MIS ranking results, the top five significant challenges to the adoption of gamification were lack of capacity and expertise, lack of budgeting for innovation, lack of technical infrastructure, hesitation to adopt and limited internet connectivity. Through factor analysis, the challenges identified were categorized into five principal clusters, namely, organizational challenges, technical-related challenges, human-related challenges, data security challenges and economic challenges. Practical implications The identification and evaluation of the key challenges hindering the adoption of gamification practices would help construction organizations and stakeholders to understand the need to embrace and implement the concept into their activities, operations and processes to improve the engagement and motivation levels of employees. Originality/value To the best of the authors’ knowledge, this study is the first of its kind in the study area to identify and evaluate the challenges affecting the adoption of gamification practices using a structured quantitative approach.
{"title":"Application of gamification for sustainable construction: an evaluation of the challenges","authors":"Ayodeji Emmanuel Oke, John Aliu, P. Tunji-Olayeni, T. Abayomi","doi":"10.1108/ci-09-2022-0247","DOIUrl":"https://doi.org/10.1108/ci-09-2022-0247","url":null,"abstract":"\u0000Purpose\u0000This paper aims to identify and evaluate the challenges affecting the adoption of gamification practices in developing countries through the lens of the Nigerian construction industry.\u0000\u0000\u0000Design/methodology/approach\u0000A scoping literature review was conducted through which challenges to the adoption of gamification practices were identified, which helped in the formulation of a questionnaire survey. Data was obtained from construction professionals including architects, builders, engineers and quantity surveyors. Retrieved data were analyzed using several statistical tools such as percentages, frequencies, mean item scores (MIS) and exploratory factor analyses.\u0000\u0000\u0000Findings\u0000Based on the MIS ranking results, the top five significant challenges to the adoption of gamification were lack of capacity and expertise, lack of budgeting for innovation, lack of technical infrastructure, hesitation to adopt and limited internet connectivity. Through factor analysis, the challenges identified were categorized into five principal clusters, namely, organizational challenges, technical-related challenges, human-related challenges, data security challenges and economic challenges.\u0000\u0000\u0000Practical implications\u0000The identification and evaluation of the key challenges hindering the adoption of gamification practices would help construction organizations and stakeholders to understand the need to embrace and implement the concept into their activities, operations and processes to improve the engagement and motivation levels of employees.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this study is the first of its kind in the study area to identify and evaluate the challenges affecting the adoption of gamification practices using a structured quantitative approach.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49547528","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}
Purpose Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level. Design/methodology/approach More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time. Findings This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry. Originality/value The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.
{"title":"Estimating labor resource requirements in construction projects using machine learning","authors":"Hamidreza Golabchi, A. Hammad","doi":"10.1108/ci-11-2021-0211","DOIUrl":"https://doi.org/10.1108/ci-11-2021-0211","url":null,"abstract":"\u0000Purpose\u0000Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level.\u0000\u0000\u0000Design/methodology/approach\u0000More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time.\u0000\u0000\u0000Findings\u0000This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry.\u0000\u0000\u0000Originality/value\u0000The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44747372","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}
Purpose Housing provides constructed space for human activities. Literature indicates that housing impacts wealth, education attainment and health outcomes, among others. Because of its contributions to society, it is essential to develop and implement strategies that address the housing shortage experienced in most cities across the globe. This study aims to unpack the factors affecting housing production in the UK and chart the way forward. Design/methodology/approach In addressing this study's aim, an interprivitst approach was adopted and semi-structured interviews were conducted with 18 experienced professionals. Data were collected across the four nations of the UK (England, Wales, Scotland and Northern Ireland). Findings The results indicated that the opportunistic behaviour of stakeholders is one of the main factors affecting housing production in the study area. Also, modern construction methods, collaborative practices, government intervention and affordable housing schemes were identified as key strategies for addressing housing production factors. Practical implications This study identified strategies for mitigating housing production issues that provide a focal point to all stakeholders keen on filling the housing shortage gap and improving productivity to channel their resources and effort accordingly. Originality/value To the best of the authors’ knowledge, this study is one of the first to empirically analyse the influencing factors on the housing gap in the UK from the perspective of the supply side to provide information that could lead towards closing the said gap.
{"title":"Towards closing the housing gap in the UK: exploration of the influencing factors and the way forward","authors":"E. Daniel, O. Oshodi, D. Dabara, Nenpin Dimka","doi":"10.1108/ci-06-2022-0148","DOIUrl":"https://doi.org/10.1108/ci-06-2022-0148","url":null,"abstract":"\u0000Purpose\u0000Housing provides constructed space for human activities. Literature indicates that housing impacts wealth, education attainment and health outcomes, among others. Because of its contributions to society, it is essential to develop and implement strategies that address the housing shortage experienced in most cities across the globe. This study aims to unpack the factors affecting housing production in the UK and chart the way forward.\u0000\u0000\u0000Design/methodology/approach\u0000In addressing this study's aim, an interprivitst approach was adopted and semi-structured interviews were conducted with 18 experienced professionals. Data were collected across the four nations of the UK (England, Wales, Scotland and Northern Ireland).\u0000\u0000\u0000Findings\u0000The results indicated that the opportunistic behaviour of stakeholders is one of the main factors affecting housing production in the study area. Also, modern construction methods, collaborative practices, government intervention and affordable housing schemes were identified as key strategies for addressing housing production factors.\u0000\u0000\u0000Practical implications\u0000This study identified strategies for mitigating housing production issues that provide a focal point to all stakeholders keen on filling the housing shortage gap and improving productivity to channel their resources and effort accordingly.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this study is one of the first to empirically analyse the influencing factors on the housing gap in the UK from the perspective of the supply side to provide information that could lead towards closing the said gap.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48542408","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}
Purpose Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method. Design/methodology/approach The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS. Findings The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem. Originality/value This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.
{"title":"Ending the suitability quantification dilemma: intelligent decision support system for modular integrated construction in a high-density metropolis","authors":"I. Y. Wuni, K. Mazher","doi":"10.1108/ci-09-2022-0242","DOIUrl":"https://doi.org/10.1108/ci-09-2022-0242","url":null,"abstract":"\u0000Purpose\u0000Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.\u0000\u0000\u0000Design/methodology/approach\u0000The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.\u0000\u0000\u0000Findings\u0000The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.\u0000\u0000\u0000Originality/value\u0000This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.\u0000","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44618767","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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