Pub Date : 2023-01-09DOI: 10.1108/sasbe-07-2022-0129
O. Ogunseiju, Nihar J. Gonsalves, A. Akanmu, Yewande Abraham, C. Nnaji
PurposeConstruction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.Design/methodology/approachThe study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.FindingsThe classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.Research limitations/implicationsThe findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.Originality/valueThe classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.
{"title":"Automated detection of learning stages and interaction difficulty from eye-tracking data within a mixed reality learning environmen","authors":"O. Ogunseiju, Nihar J. Gonsalves, A. Akanmu, Yewande Abraham, C. Nnaji","doi":"10.1108/sasbe-07-2022-0129","DOIUrl":"https://doi.org/10.1108/sasbe-07-2022-0129","url":null,"abstract":"PurposeConstruction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.Design/methodology/approachThe study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.FindingsThe classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.Research limitations/implicationsThe findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.Originality/valueThe classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44916175","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 : 2022-12-29DOI: 10.1108/sasbe-09-2022-0204
E. Rasheed, J. Rotimi
PurposeAchieving an appropriate indoor environment quality (IEQ) is crucial to a green office environment. Whilst much research has been carried out across the globe on the ideal IEQ for green offices, little is known about which indoor environment New Zealand office workers prefer and regard as most appropriate. This study investigated New Zealand office workers' preference for a green environment.Design/methodology/approachWorkers were conveniently selected for a questionnaire survey study from two major cities in the country – Wellington and Auckland. The perception of 149 workers was analysed and discussed based on the workers' demographics. The responses to each question were analysed based on the mean, standard deviation, frequency of responses and difference in opinion.FindingsThe results showed that workers' preferences for an ideal IEQ in green work environments depend largely on demographics. New Zealand office workers prefer work environments to have more fresh air and rely on mixed-mode ventilation and lighting systems. Also New Zealand office workers like to have better acoustic quality with less distraction and background noise. Regarding temperature, workers prefer workspaces to be neither cooler nor warmer. Unique to New Zealand workers, the workers prefer to have some (not complete) individual control over the IEQ in offices.Research limitations/implicationsThis study was conducted in the summer season, which could have impacted the responses received. Also the sample size was limited to two major cities in the country. Further studies should be conducted in other regions and during different seasons.Practical implicationsThis study provides the opportunity for more studies in this area of research and highlights significant findings worthy of critical investigations. The results of this study benefit various stakeholders, such as facilities managers and workplace designers, and support proactive response approaches to achieving building occupants' preferences for an ideal work environment.Originality/valueThis study is the first research in New Zealand to explore worker preferences of IEQ that is not limited to a particular building, expanding the body of knowledge on workers' perception of the ideal work environment in the country.
{"title":"The green office environment: New Zealand workers' perception of IEQ","authors":"E. Rasheed, J. Rotimi","doi":"10.1108/sasbe-09-2022-0204","DOIUrl":"https://doi.org/10.1108/sasbe-09-2022-0204","url":null,"abstract":"PurposeAchieving an appropriate indoor environment quality (IEQ) is crucial to a green office environment. Whilst much research has been carried out across the globe on the ideal IEQ for green offices, little is known about which indoor environment New Zealand office workers prefer and regard as most appropriate. This study investigated New Zealand office workers' preference for a green environment.Design/methodology/approachWorkers were conveniently selected for a questionnaire survey study from two major cities in the country – Wellington and Auckland. The perception of 149 workers was analysed and discussed based on the workers' demographics. The responses to each question were analysed based on the mean, standard deviation, frequency of responses and difference in opinion.FindingsThe results showed that workers' preferences for an ideal IEQ in green work environments depend largely on demographics. New Zealand office workers prefer work environments to have more fresh air and rely on mixed-mode ventilation and lighting systems. Also New Zealand office workers like to have better acoustic quality with less distraction and background noise. Regarding temperature, workers prefer workspaces to be neither cooler nor warmer. Unique to New Zealand workers, the workers prefer to have some (not complete) individual control over the IEQ in offices.Research limitations/implicationsThis study was conducted in the summer season, which could have impacted the responses received. Also the sample size was limited to two major cities in the country. Further studies should be conducted in other regions and during different seasons.Practical implicationsThis study provides the opportunity for more studies in this area of research and highlights significant findings worthy of critical investigations. The results of this study benefit various stakeholders, such as facilities managers and workplace designers, and support proactive response approaches to achieving building occupants' preferences for an ideal work environment.Originality/valueThis study is the first research in New Zealand to explore worker preferences of IEQ that is not limited to a particular building, expanding the body of knowledge on workers' perception of the ideal work environment in the country.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44170421","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 : 2022-12-12DOI: 10.1108/sasbe-06-2022-0113
G. Wusu, H. Alaka, W. Yusuf, Iofis Mporas, L. Toriola-Coker, Raphael Oseghale
PurposeSeveral factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.Design/methodology/approachThe research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).FindingsThe research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.Research limitations/implicationsData were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.Practical implicationsThe research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.Originality/valueThe research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.
{"title":"A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria","authors":"G. Wusu, H. Alaka, W. Yusuf, Iofis Mporas, L. Toriola-Coker, Raphael Oseghale","doi":"10.1108/sasbe-06-2022-0113","DOIUrl":"https://doi.org/10.1108/sasbe-06-2022-0113","url":null,"abstract":"PurposeSeveral factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.Design/methodology/approachThe research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).FindingsThe research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.Research limitations/implicationsData were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.Practical implicationsThe research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.Originality/valueThe research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45663100","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 : 2022-12-12DOI: 10.1108/sasbe-08-2022-0170
Abood Khaled Alamoudi, R. Abidoye, Terence Y. M. Lam
PurposeThe smart sustainable cities (SSC) concept has a wide acknowledgement amongst governments and societies that deal with emerging technology and help in developing better urban communities. However, the fact that citizens' participation (CP) is not adherent to the current policies and governance often boosts their aspirations of decision-making to become smart cities. This paper aims to identify SSC variables and, more importantly, rank, categorise and discuss the factors towards implementing SSC by engaging, empowering and enabling citizens to participate in the urban development of SSC.Design/methodology/approachA comprehensive literature review identified 38 factors in the CP process. Those factors were used to design an online questionnaire administered to the respondents. A total of 164 valid responses were collected. A two-stage statistical analysis was adopted. First, the Relative Importance Index (RII) was used to rank and prioritise the importance of the factors that affect the current policies and agenda. Second, factor analysis was utilised to categorise and group those factors.FindingsThis study founds four significant factors that help in implanting SSC: “knowledge of smart sustainable cities”, “awareness of smart sustainable cities”, “willingness of the citizens to participate” and “opinion on the current agenda of the government's role”.Research limitations/implicationsThis study has a few limitations which can be considered in future studies. First, the response rate of the participant is relatively low (163), so sampling a larger segment will support the broader perception of the citizens.Practical implicationsThe outcome of this paper underlines the need for the successful implementation of smart cities by adopting CP in the process of impacting policies and governance. Particularly, it identifies factors that help cities and policymakers in engaging CP in developing new policies and revising existing policies for promoting SSC.Originality/valueThere is a need to investigate the most critical factors that influence CP for implementing SSC. These factors have not been adequately examined in extant literature.
{"title":"An evaluation of stakeholders' participation process in developing smart sustainable cities in Saudi Arabia","authors":"Abood Khaled Alamoudi, R. Abidoye, Terence Y. M. Lam","doi":"10.1108/sasbe-08-2022-0170","DOIUrl":"https://doi.org/10.1108/sasbe-08-2022-0170","url":null,"abstract":"PurposeThe smart sustainable cities (SSC) concept has a wide acknowledgement amongst governments and societies that deal with emerging technology and help in developing better urban communities. However, the fact that citizens' participation (CP) is not adherent to the current policies and governance often boosts their aspirations of decision-making to become smart cities. This paper aims to identify SSC variables and, more importantly, rank, categorise and discuss the factors towards implementing SSC by engaging, empowering and enabling citizens to participate in the urban development of SSC.Design/methodology/approachA comprehensive literature review identified 38 factors in the CP process. Those factors were used to design an online questionnaire administered to the respondents. A total of 164 valid responses were collected. A two-stage statistical analysis was adopted. First, the Relative Importance Index (RII) was used to rank and prioritise the importance of the factors that affect the current policies and agenda. Second, factor analysis was utilised to categorise and group those factors.FindingsThis study founds four significant factors that help in implanting SSC: “knowledge of smart sustainable cities”, “awareness of smart sustainable cities”, “willingness of the citizens to participate” and “opinion on the current agenda of the government's role”.Research limitations/implicationsThis study has a few limitations which can be considered in future studies. First, the response rate of the participant is relatively low (163), so sampling a larger segment will support the broader perception of the citizens.Practical implicationsThe outcome of this paper underlines the need for the successful implementation of smart cities by adopting CP in the process of impacting policies and governance. Particularly, it identifies factors that help cities and policymakers in engaging CP in developing new policies and revising existing policies for promoting SSC.Originality/valueThere is a need to investigate the most critical factors that influence CP for implementing SSC. These factors have not been adequately examined in extant literature.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45560916","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 : 2022-12-07DOI: 10.1108/sasbe-07-2022-0152
Fatemeh Mostafavi, M. Tahsildoost, Z. Zomorodian, Seyed Shayan Shahrestani
PurposeIn this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design.Design/methodology/approachA methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth.FindingsThe results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds.Originality/valueThe proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.
{"title":"An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design","authors":"Fatemeh Mostafavi, M. Tahsildoost, Z. Zomorodian, Seyed Shayan Shahrestani","doi":"10.1108/sasbe-07-2022-0152","DOIUrl":"https://doi.org/10.1108/sasbe-07-2022-0152","url":null,"abstract":"PurposeIn this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design.Design/methodology/approachA methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth.FindingsThe results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds.Originality/valueThe proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47619088","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 : 2022-12-06DOI: 10.1108/sasbe-05-2022-0086
Hanane Bouhmoud, D. Loudyi, S. Azhar
PurposeConsidering the world population, an additional 415.1 billion m2 of built floor will be needed by 2050, which could worsen the environmental impact of the construction industry that is responsible for one-third of global Carbon Emissions (CEs). Thus, the current construction practices need to be upgraded toward eco-friendly technologies. Building Information Modeling (BIM) proved a significant potential to enhance Building and Infrastructure (B&I) ecological performances. However, no previous study has evaluated the nexus between BIM and B&I CEs. This study aims to fill this gap by disclosing the research evolution and metrics and key concepts and tools associated with this nexus.Design/methodology/approachA mixed-method design was adopted based on scientometric and scoping reviews of 52 consistent peer-reviewed papers collected from 3 large scientific databases.FindingsThis study presented six research metrics and revealed that the nexus between BIM and CEs is a contemporary topic that involves seven main research themes. Moreover, it cast light on six key associated concepts: Life Cycle Assessment; Boundary limits; Building Life Cycle CE (BLCCE); Responsible sources for BLCCE; Green and integrated BIM; and sustainable buildings and related rating systems. Furthermore, it identified 56 nexus-related Information and Communication Technologies tools and 17 CE-coefficient databases and discussed their consistency.Originality/valueThis study will fill the knowledge gap by providing scholars, practitioners and decision-makers with a good grasp of the nexus between CEs and BIM and paving the path toward further research, strategies and technological solutions to decrease CEs of B&I sectors and their impacts on the climate change.
{"title":"Building information modeling (BIM) for lifecycle carbon emission: scientometric and scoping literature reviews","authors":"Hanane Bouhmoud, D. Loudyi, S. Azhar","doi":"10.1108/sasbe-05-2022-0086","DOIUrl":"https://doi.org/10.1108/sasbe-05-2022-0086","url":null,"abstract":"PurposeConsidering the world population, an additional 415.1 billion m2 of built floor will be needed by 2050, which could worsen the environmental impact of the construction industry that is responsible for one-third of global Carbon Emissions (CEs). Thus, the current construction practices need to be upgraded toward eco-friendly technologies. Building Information Modeling (BIM) proved a significant potential to enhance Building and Infrastructure (B&I) ecological performances. However, no previous study has evaluated the nexus between BIM and B&I CEs. This study aims to fill this gap by disclosing the research evolution and metrics and key concepts and tools associated with this nexus.Design/methodology/approachA mixed-method design was adopted based on scientometric and scoping reviews of 52 consistent peer-reviewed papers collected from 3 large scientific databases.FindingsThis study presented six research metrics and revealed that the nexus between BIM and CEs is a contemporary topic that involves seven main research themes. Moreover, it cast light on six key associated concepts: Life Cycle Assessment; Boundary limits; Building Life Cycle CE (BLCCE); Responsible sources for BLCCE; Green and integrated BIM; and sustainable buildings and related rating systems. Furthermore, it identified 56 nexus-related Information and Communication Technologies tools and 17 CE-coefficient databases and discussed their consistency.Originality/valueThis study will fill the knowledge gap by providing scholars, practitioners and decision-makers with a good grasp of the nexus between CEs and BIM and paving the path toward further research, strategies and technological solutions to decrease CEs of B&I sectors and their impacts on the climate change.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45581009","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 : 2022-11-29DOI: 10.1108/sasbe-07-2022-0139
Nazanin Kordestani Ghalenoei, M. Babaeian Jelodar, Daniel Paes, M. Sutrisna
PurposeThe development of prefabrication into full-scale offsite manufacturing processes in the construction industry is paradigm-shifting. Moreover, Building Information Modelling (BIM) is becoming the primary mode of communication and integration in construction projects to facilitate the flow of information. Although research has been performed on BIM and Offsite Construction (OSC), integrating these two concepts remains ambiguous and complex and lacks documentation and structure, especially in New Zealand. Therefore, this paper develops a robust framework for OSC and BIM integration. The study focusses on identifying integration challenges and proposes strategies for overcoming these challenges.Design/methodology/approachThis study applied scientometric analysis, a systematic literature review (SLR) and semi-structured expert interviews to investigate OSC and BIM integration challenges. Multiple themes were investigated and triangulation conducted in this research supports the creation of applicable knowledge in this field.FindingsMultiple gaps, research trends and the pioneer countries in the paper's scope have been identified through scientometric analysis. Then, a classified cluster of challenges for OSC and BIM implementation and integration strategies of OSC and BIM were demonstrated from the findings. The interviews provided comprehensive and complementary data sets and analyses. The findings from the Systematic Literature Review and interview structured the integration framework.Originality/valueThe contribution of this paper to existing knowledge is a developed framework that serves as a guideline for the OSC stakeholders. This framework can assess OSC's alignment with BIM and consolidate strategies for incorporating OSC into a BIM-based project delivery process. The framework consists of 23 strategies categorised into 8 clusters: a policy document, training and professional development, documentation, technology management, governmental development, contract development, accurate definition and detailing and communication. The proposed strategies will streamline integration by reducing potential challenges, thus enhancing project productivity.
{"title":"Challenges of offsite construction and BIM implementation: providing a framework for integration in New Zealand","authors":"Nazanin Kordestani Ghalenoei, M. Babaeian Jelodar, Daniel Paes, M. Sutrisna","doi":"10.1108/sasbe-07-2022-0139","DOIUrl":"https://doi.org/10.1108/sasbe-07-2022-0139","url":null,"abstract":"PurposeThe development of prefabrication into full-scale offsite manufacturing processes in the construction industry is paradigm-shifting. Moreover, Building Information Modelling (BIM) is becoming the primary mode of communication and integration in construction projects to facilitate the flow of information. Although research has been performed on BIM and Offsite Construction (OSC), integrating these two concepts remains ambiguous and complex and lacks documentation and structure, especially in New Zealand. Therefore, this paper develops a robust framework for OSC and BIM integration. The study focusses on identifying integration challenges and proposes strategies for overcoming these challenges.Design/methodology/approachThis study applied scientometric analysis, a systematic literature review (SLR) and semi-structured expert interviews to investigate OSC and BIM integration challenges. Multiple themes were investigated and triangulation conducted in this research supports the creation of applicable knowledge in this field.FindingsMultiple gaps, research trends and the pioneer countries in the paper's scope have been identified through scientometric analysis. Then, a classified cluster of challenges for OSC and BIM implementation and integration strategies of OSC and BIM were demonstrated from the findings. The interviews provided comprehensive and complementary data sets and analyses. The findings from the Systematic Literature Review and interview structured the integration framework.Originality/valueThe contribution of this paper to existing knowledge is a developed framework that serves as a guideline for the OSC stakeholders. This framework can assess OSC's alignment with BIM and consolidate strategies for incorporating OSC into a BIM-based project delivery process. The framework consists of 23 strategies categorised into 8 clusters: a policy document, training and professional development, documentation, technology management, governmental development, contract development, accurate definition and detailing and communication. The proposed strategies will streamline integration by reducing potential challenges, thus enhancing project productivity.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":"1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62315308","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 : 2022-11-24DOI: 10.1108/sasbe-07-2022-0130
Nihar J. Gonsalves, O. Ogunseiju, A. Akanmu
PurposeRecognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.Design/methodology/approachA lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.FindingsResults indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.Research limitations/implicationsThe study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.Originality/valueThis study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.
{"title":"Activity recognition from trunk muscle activations for wearable and non-wearable robot conditions","authors":"Nihar J. Gonsalves, O. Ogunseiju, A. Akanmu","doi":"10.1108/sasbe-07-2022-0130","DOIUrl":"https://doi.org/10.1108/sasbe-07-2022-0130","url":null,"abstract":"PurposeRecognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.Design/methodology/approachA lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.FindingsResults indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.Research limitations/implicationsThe study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.Originality/valueThis study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42160074","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 : 2022-11-23DOI: 10.1108/sasbe-08-2022-0177
M. Burfoot, A. Ghaffarianhoseini, Amirhosein Ghaffarianhoseini, N. Naismith
PurposeTo maximise acoustic comfort in a classroom, the acoustic conditions of the space should be variable. So, the optimal acoustic state also changes when the classroom changes from a study environment into a lecture environment. Passive Variable Acoustic Technology (PVAT) alters a room’s Reverberation Time (RT) by changing the total sound absorption in a room. The purpose of this paper is to evaluate the improvements to classroom acoustic comfort when using PVAT.Design/methodology/approachThe study is conducted in an existing tertiary classroom at Auckland University of Technology, New Zealand. The PVAT is prototyped, and the RTs are measured according to international standards before and after classroom installation. The acoustic measurement method used is a cost-effective application tool where pre- and post-conditions are of primary concern.FindingsPVAT is found to offer statistically significant improvements in RT, but the key benefits are realised in its’ ability to vary RT for different classroom situations. It is predicted that the RT recommendations for two room types outlined in the acoustic standard AS/NZS 2107:2016 are satisfied when using PVAT in a single classroom space. By optimising RT, the acoustic comfort during both study and lecture is significantly improved.Originality/valueWhen PVAT is combined with an intelligent system – Intelligent Passive Room Acoustic Technology (IPRAT) – it can detect sound waves in real time to identify the optimal RT. This paper details a pilot case study that works towards quantifying the benefits of IPRAT, by prototyping and testing the PVAT component of the system.Highlights A pilot case study outlines the development and test of a variable acoustic prototype in a tertiary classroomA method is adopted to measure acoustic conditions, using three under-researched Android applicationsThe benefits of PVAT are realised in its ability to vary RT by adjusting the prototypes’ sound absorptionBy using PVAT in a single space, the recommended RTs for two room types outlined in the acoustic standard AS/NZS 2107:2016 can be satisfiedThe improvements in acoustic comfort due to PVAT are statistically significant
{"title":"Passive variable acoustic technology for classroom reverberation time: a case study","authors":"M. Burfoot, A. Ghaffarianhoseini, Amirhosein Ghaffarianhoseini, N. Naismith","doi":"10.1108/sasbe-08-2022-0177","DOIUrl":"https://doi.org/10.1108/sasbe-08-2022-0177","url":null,"abstract":"PurposeTo maximise acoustic comfort in a classroom, the acoustic conditions of the space should be variable. So, the optimal acoustic state also changes when the classroom changes from a study environment into a lecture environment. Passive Variable Acoustic Technology (PVAT) alters a room’s Reverberation Time (RT) by changing the total sound absorption in a room. The purpose of this paper is to evaluate the improvements to classroom acoustic comfort when using PVAT.Design/methodology/approachThe study is conducted in an existing tertiary classroom at Auckland University of Technology, New Zealand. The PVAT is prototyped, and the RTs are measured according to international standards before and after classroom installation. The acoustic measurement method used is a cost-effective application tool where pre- and post-conditions are of primary concern.FindingsPVAT is found to offer statistically significant improvements in RT, but the key benefits are realised in its’ ability to vary RT for different classroom situations. It is predicted that the RT recommendations for two room types outlined in the acoustic standard AS/NZS 2107:2016 are satisfied when using PVAT in a single classroom space. By optimising RT, the acoustic comfort during both study and lecture is significantly improved.Originality/valueWhen PVAT is combined with an intelligent system – Intelligent Passive Room Acoustic Technology (IPRAT) – it can detect sound waves in real time to identify the optimal RT. This paper details a pilot case study that works towards quantifying the benefits of IPRAT, by prototyping and testing the PVAT component of the system.Highlights A pilot case study outlines the development and test of a variable acoustic prototype in a tertiary classroomA method is adopted to measure acoustic conditions, using three under-researched Android applicationsThe benefits of PVAT are realised in its ability to vary RT by adjusting the prototypes’ sound absorptionBy using PVAT in a single space, the recommended RTs for two room types outlined in the acoustic standard AS/NZS 2107:2016 can be satisfiedThe improvements in acoustic comfort due to PVAT are statistically significant","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42711598","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 : 2022-11-22DOI: 10.1108/sasbe-03-2022-0048
Florence Dadzoe, M. Addy, D. Duah, M. Adesi
PurposeTo be able to achieve the uptake and usage of green buildings requires various actors within the construction value chain to be engaged. Despite its global uptake, green building construction is still at its nascent stage in Ghana. Most studies in sub-Saharan Africa point to the lack of knowledge as one of the mitigating factors against its development. However, there is a dearth of studies assessing the level of knowledge of stakeholders. The terms “knowledge” and “awareness” of green building construction are often used interchangeably in the Ghanaian Construction Industry (GCI). This study seeks to unearth the level of knowledge of stakeholders on green building construction through a comparative analysis of construction professionals and demand-side operators.Design/methodology/approachA structured questionnaire was issued to professionals in the various recognised bodies in the construction industry and public and private institutions in Ghana. Frequency, Kolmogorov–Smirnov test, median statistics and Mann–Whitney U-Test were used to rank and analyse the level of knowledge of stakeholders.FindingsConstruction professionals were more aware of green building construction than the demand-side operators. It was further identified that only a few of these stakeholders had hands-on experience as the majority of them have gained their awareness through research studies. Based on the findings of the study, it was revealed that the concept of green building construction is more abstract to stakeholders than practical despite their positive attitude towards its adoption.Practical implicationsContextually, the study has aided in showing the level of knowledge of stakeholders on green building construction. The findings of the study aside from it aiding policymakers have also helped in identifying the perceptions and attitudes of stakeholders, their strengths and weakness in green building construction. It is recommended that due to the differences in socio-political structures and construction methods, a clear definition of green building based on the availability of resources in the GCI will encourage its adoption.Originality/valueThe study used two stakeholder groupings in the GCI as the unit of analysis. This enabled insightful discoveries into the knowledge-attitude gap of Ghanaian stakeholders that are driving the adoption of green building.
{"title":"Towards a circular economy: a knowledge-attitude gap between demand and supply-side operators on green building construction in Ghana","authors":"Florence Dadzoe, M. Addy, D. Duah, M. Adesi","doi":"10.1108/sasbe-03-2022-0048","DOIUrl":"https://doi.org/10.1108/sasbe-03-2022-0048","url":null,"abstract":"PurposeTo be able to achieve the uptake and usage of green buildings requires various actors within the construction value chain to be engaged. Despite its global uptake, green building construction is still at its nascent stage in Ghana. Most studies in sub-Saharan Africa point to the lack of knowledge as one of the mitigating factors against its development. However, there is a dearth of studies assessing the level of knowledge of stakeholders. The terms “knowledge” and “awareness” of green building construction are often used interchangeably in the Ghanaian Construction Industry (GCI). This study seeks to unearth the level of knowledge of stakeholders on green building construction through a comparative analysis of construction professionals and demand-side operators.Design/methodology/approachA structured questionnaire was issued to professionals in the various recognised bodies in the construction industry and public and private institutions in Ghana. Frequency, Kolmogorov–Smirnov test, median statistics and Mann–Whitney U-Test were used to rank and analyse the level of knowledge of stakeholders.FindingsConstruction professionals were more aware of green building construction than the demand-side operators. It was further identified that only a few of these stakeholders had hands-on experience as the majority of them have gained their awareness through research studies. Based on the findings of the study, it was revealed that the concept of green building construction is more abstract to stakeholders than practical despite their positive attitude towards its adoption.Practical implicationsContextually, the study has aided in showing the level of knowledge of stakeholders on green building construction. The findings of the study aside from it aiding policymakers have also helped in identifying the perceptions and attitudes of stakeholders, their strengths and weakness in green building construction. It is recommended that due to the differences in socio-political structures and construction methods, a clear definition of green building based on the availability of resources in the GCI will encourage its adoption.Originality/valueThe study used two stakeholder groupings in the GCI as the unit of analysis. This enabled insightful discoveries into the knowledge-attitude gap of Ghanaian stakeholders that are driving the adoption of green building.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48200780","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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