Pub Date : 2024-01-16DOI: 10.1108/sasbe-08-2023-0226
Nasim Babazadeh, Jochen Teizer, H. Bargstädt, Jürgen Melzner
PurposeConstruction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.Design/methodology/approachThe paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.FindingsThe results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.Originality/valueWhile previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.
{"title":"Predictive simulation of construction site noise emissions from heavy equipment","authors":"Nasim Babazadeh, Jochen Teizer, H. Bargstädt, Jürgen Melzner","doi":"10.1108/sasbe-08-2023-0226","DOIUrl":"https://doi.org/10.1108/sasbe-08-2023-0226","url":null,"abstract":"PurposeConstruction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.Design/methodology/approachThe paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.FindingsThe results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.Originality/valueWhile previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139619331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.1108/sasbe-05-2023-0131
Spencer Li Ern Teo, Yuhan Zhou, Justin K.W. Yeoh
PurposeNetwork coverage is crucial for the adoption of advanced Smart Home applications. The commonly used log-based path loss model is not able to accurately estimate WiFi signal strength in different houses, as it does not fully consider the impact of building morphology. To better describe the propagation of WiFi signals and achieve higher estimation accuracy, this paper studies the basic building morphology characteristics of houses.Design/methodology/approachA new path loss model based on a decision tree was proposed after measuring the WiFi signal strength passing through multiple housing units. Three types of regression models were tested and compared.FindingsThe findings demonstrate that the log-based path loss model fits small houses well, while the newly proposed nonlinear path loss model performs better in large houses (area larger than 125 m2 and area-to-perimeter ratio larger than 2.5). The impact of building design on path loss has been proven and specifically quantified in the model.Originality/valueProposed an improved model to estimate indoor network coverage. Quantify the impacts of building morphology on indoor WiFi signal strength. Improve WiFi signal strength estimation to support Smart Home applications.
{"title":"Indoor WiFi path loss model to estimate indoor network coverage considering residential design","authors":"Spencer Li Ern Teo, Yuhan Zhou, Justin K.W. Yeoh","doi":"10.1108/sasbe-05-2023-0131","DOIUrl":"https://doi.org/10.1108/sasbe-05-2023-0131","url":null,"abstract":"PurposeNetwork coverage is crucial for the adoption of advanced Smart Home applications. The commonly used log-based path loss model is not able to accurately estimate WiFi signal strength in different houses, as it does not fully consider the impact of building morphology. To better describe the propagation of WiFi signals and achieve higher estimation accuracy, this paper studies the basic building morphology characteristics of houses.Design/methodology/approachA new path loss model based on a decision tree was proposed after measuring the WiFi signal strength passing through multiple housing units. Three types of regression models were tested and compared.FindingsThe findings demonstrate that the log-based path loss model fits small houses well, while the newly proposed nonlinear path loss model performs better in large houses (area larger than 125 m2 and area-to-perimeter ratio larger than 2.5). The impact of building design on path loss has been proven and specifically quantified in the model.Originality/valueProposed an improved model to estimate indoor network coverage. Quantify the impacts of building morphology on indoor WiFi signal strength. Improve WiFi signal strength estimation to support Smart Home applications.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139622155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.1108/sasbe-09-2023-0251
Faris Elghaish, Sandra Tawfiq Matarneh, Essam Abdellatef, F. Rahimian, M. R. Hosseini, Ahmed Farouk Kineber
PurposeCracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.Design/methodology/approachTo enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.FindingsThe study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.Practical implicationsWith an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.Originality/valueThe proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.
{"title":"Multi-layers deep learning model with feature selection for automated detection and classification of highway pavement cracks","authors":"Faris Elghaish, Sandra Tawfiq Matarneh, Essam Abdellatef, F. Rahimian, M. R. Hosseini, Ahmed Farouk Kineber","doi":"10.1108/sasbe-09-2023-0251","DOIUrl":"https://doi.org/10.1108/sasbe-09-2023-0251","url":null,"abstract":"PurposeCracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.Design/methodology/approachTo enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.FindingsThe study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.Practical implicationsWith an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.Originality/valueThe proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-12DOI: 10.1108/sasbe-10-2022-0224
A. Rashidi, George Lukic Woon, Miyami Dasandara, Mohsen Bazghaleh, P. Pasbakhsh
Purpose The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.Design/methodology/approach The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.Findings This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.Originality/valueThe proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems
{"title":"Smart personal protective equipment for intelligent construction safety monitoring","authors":"A. Rashidi, George Lukic Woon, Miyami Dasandara, Mohsen Bazghaleh, P. Pasbakhsh","doi":"10.1108/sasbe-10-2022-0224","DOIUrl":"https://doi.org/10.1108/sasbe-10-2022-0224","url":null,"abstract":"Purpose The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.Design/methodology/approach The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.Findings This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.Originality/valueThe proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-11DOI: 10.1108/sasbe-12-2023-0377
Andrei Khurshudov
PurposeA smart city integrates a comprehensive suite of technologies, which inherently require data to function effectively. It is designed with the intention of amassing all available data concerning machines, devices, infrastructure, individuals and their surroundings. This commentary addresses the evolution of smart cities over time, the increasing extent of data collection, the growing pressure on personal privacy and people's reactions to these trends. The article highlights the contradiction between the needs of the city and the desires of its inhabitants. It notes that people react differently to gradual versus abrupt changes in data-collecting technologies and services. It also suggests that more work needs to be done to prepare both smart cities and the human population for a sustainable, mutually beneficial future.Design/methodology/approachThis commentary presents a viewpoint on the subject of data collection and privacy in smart cities, drawing on various sources to support its observations and conclusions.FindingsThe primary focus of this discussion is on the technological evolution of the cities. It emphasizes that, as cities get smarter, they offer more conveniences in exchange for various types of data, highlighting the likelihood that pressure on personal privacy will continue to escalate. This is due to the increasing pervasiveness of data-collecting technologies in every aspect of lives and urban environments. These environments are expected to become progressively smarter each year. Given this context, and to ensure a seamless transition to smart and sustainable cities, it is imperative that today's privacy discussions start to focus not only just on the existing but also on the future conditions and challenges that citizens are expected to encounter.Originality/valueThis commentary delves into the existing gaps in understanding the contradiction between the data-collection “needs” of smart cities, the direction in which the cities are evolving and people's awareness of how much data they will have to surrender in the future. It also highlights the risk of people gradually relinquishing nearly all their privacy, often without noticing, in exchange for the ever-increasing conveniences offered by smart cities.
{"title":"The smart city conundrum: technology, privacy, and the quest for convenience","authors":"Andrei Khurshudov","doi":"10.1108/sasbe-12-2023-0377","DOIUrl":"https://doi.org/10.1108/sasbe-12-2023-0377","url":null,"abstract":"PurposeA smart city integrates a comprehensive suite of technologies, which inherently require data to function effectively. It is designed with the intention of amassing all available data concerning machines, devices, infrastructure, individuals and their surroundings. This commentary addresses the evolution of smart cities over time, the increasing extent of data collection, the growing pressure on personal privacy and people's reactions to these trends. The article highlights the contradiction between the needs of the city and the desires of its inhabitants. It notes that people react differently to gradual versus abrupt changes in data-collecting technologies and services. It also suggests that more work needs to be done to prepare both smart cities and the human population for a sustainable, mutually beneficial future.Design/methodology/approachThis commentary presents a viewpoint on the subject of data collection and privacy in smart cities, drawing on various sources to support its observations and conclusions.FindingsThe primary focus of this discussion is on the technological evolution of the cities. It emphasizes that, as cities get smarter, they offer more conveniences in exchange for various types of data, highlighting the likelihood that pressure on personal privacy will continue to escalate. This is due to the increasing pervasiveness of data-collecting technologies in every aspect of lives and urban environments. These environments are expected to become progressively smarter each year. Given this context, and to ensure a seamless transition to smart and sustainable cities, it is imperative that today's privacy discussions start to focus not only just on the existing but also on the future conditions and challenges that citizens are expected to encounter.Originality/valueThis commentary delves into the existing gaps in understanding the contradiction between the data-collection “needs” of smart cities, the direction in which the cities are evolving and people's awareness of how much data they will have to surrender in the future. It also highlights the risk of people gradually relinquishing nearly all their privacy, often without noticing, in exchange for the ever-increasing conveniences offered by smart cities.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1108/sasbe-03-2023-0058
Ken Farnes, Neville Hurst, Woon-Weng Wong, S. Wilkinson
PurposeThe purpose of this study was to explore and critique the benefits and disbenefits that transport orientated development (TOD) brings to neighbourhoods in proximity to public transport hubs.Design/methodology/approachThis is an exploratory study that may also be described as a rapid review that aims to provide coverage of the available literature in a systematic process that is simplified to produce information in a timely manner. Due to the relatively small number of available studies from peer-reviewed sources, the variety of methods and data used and the constrained time available for this study, the study did not immediately lend itself to a more thorough systematic literature review.FindingsThe literature shows the discourse on TOD upholds its promise to create a high-density mixed-use walkable neighbourhood supported by transport infrastructure, increasing accessibility, minimising vehicle dependency, reducing traffic congestion, moderating urban sprawl and reducing pollution. There are few articles on the negative aspects of TOD, particularly concerning social exclusion, crime, sustainability and concerns about gentrification of neighbourhoods.Research limitations/implicationsThe study did not immediately lend itself to a more thorough systematic literature review due to the relatively small number of available studies, the variety of methods and data used and the constrained time available for this study.Originality/valueThis study allows social investigators, policymakers and developers understand the benefits and disbenefits of TOD including policy implications regarding potential criminogenic factors.
本研究的目的是探讨和评论以交通为导向的开发（TOD）给公共交通枢纽附近社区带来的益处和弊端。设计/方法/方法这是一项探索性研究，也可以说是一项快速综述，旨在通过简化的系统过程提供现有文献的覆盖范围，以便及时提供信息。由于同行评审来源的可用研究数量相对较少，所使用的方法和数据多种多样，以及本研究的时间有限，因此本研究无法立即进行更彻底的系统性文献综述。研究结果文献显示，关于 TOD 的讨论坚持了其承诺，即在交通基础设施的支持下，创建一个高密度、多功能、可步行的街区，增加可达性，最大限度地减少对车辆的依赖，减少交通拥堵，减缓城市扩张并减少污染。关于 TOD 负面影响的文章很少，尤其是在社会排斥、犯罪、可持续性以及对社区绅士化的担忧方面。研究局限性/影响由于现有研究数量相对较少，使用的方法和数据多种多样，而且研究时间有限，因此本研究无法立即进行更彻底的系统性文献综述。
{"title":"An exploratory study on the benefits of transit orientated development (TOD) to rail infrastructure projects","authors":"Ken Farnes, Neville Hurst, Woon-Weng Wong, S. Wilkinson","doi":"10.1108/sasbe-03-2023-0058","DOIUrl":"https://doi.org/10.1108/sasbe-03-2023-0058","url":null,"abstract":"PurposeThe purpose of this study was to explore and critique the benefits and disbenefits that transport orientated development (TOD) brings to neighbourhoods in proximity to public transport hubs.Design/methodology/approachThis is an exploratory study that may also be described as a rapid review that aims to provide coverage of the available literature in a systematic process that is simplified to produce information in a timely manner. Due to the relatively small number of available studies from peer-reviewed sources, the variety of methods and data used and the constrained time available for this study, the study did not immediately lend itself to a more thorough systematic literature review.FindingsThe literature shows the discourse on TOD upholds its promise to create a high-density mixed-use walkable neighbourhood supported by transport infrastructure, increasing accessibility, minimising vehicle dependency, reducing traffic congestion, moderating urban sprawl and reducing pollution. There are few articles on the negative aspects of TOD, particularly concerning social exclusion, crime, sustainability and concerns about gentrification of neighbourhoods.Research limitations/implicationsThe study did not immediately lend itself to a more thorough systematic literature review due to the relatively small number of available studies, the variety of methods and data used and the constrained time available for this study.Originality/valueThis study allows social investigators, policymakers and developers understand the benefits and disbenefits of TOD including policy implications regarding potential criminogenic factors.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.1108/sasbe-02-2023-0044
Emma Mihocic, K. Gharehbaghi, Per Hilletofth, Kong Fah Tee, Matt Myers
PurposeIn successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).Design/methodology/approachAs the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.FindingsUsing factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.Originality/valueThis paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.
{"title":"Augmenting the cities’ and metropolitan regional demands for mega rail infrastructure: the application of SWOT and factor analysis","authors":"Emma Mihocic, K. Gharehbaghi, Per Hilletofth, Kong Fah Tee, Matt Myers","doi":"10.1108/sasbe-02-2023-0044","DOIUrl":"https://doi.org/10.1108/sasbe-02-2023-0044","url":null,"abstract":"PurposeIn successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).Design/methodology/approachAs the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.FindingsUsing factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.Originality/valueThis paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139124879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1108/sasbe-07-2023-0195
Abdulbasit Almhafdy, A. Alsehail
PurposeThis paper investigates the optimization of window design factors (WDFs) in hospital buildings, particularly in government hospitals within the arid climate of the Qassim region, with the aim of achieving a better cooling load reduction. Continuous monitoring of the hospital ward section is crucial due to patients' needs, requiring optimal indoor air quality and cooling load.Design/methodology/approachThe study identifies the optimal conditions for WDF design to mitigate cooling load, including window-to-wall ratio (WWR), window orientation (WO), room size and U-value (thermal properties), effectively reduce energy consumption in terms of sensible cooling load (MWh/m2) and comply with local codes. Data collection involved a smart weather station, while the Integrated Environmental Solution Virtual Environment (IESVE) software facilitated the simulation process.FindingsKey findings reveal that larger patient rooms were about 40% more energy-efficient than smaller rooms. The northern orientation showed lower energy consumption, and specific WWRs and glazing U-values significantly affected energy loads. In an analysis of U-value changes in energy performance based on the Saudi Building Code (SBC), the presented values did not meet the minimum energy consumption standards. For a valid 40% WWR with a thermal permeability of 2.89, 0.181 MWh/m2 was consumed, while for an invalid 100% WWR with the same permeability but facing the north, 0.156 MWh/m2 was consumed, which is considered an invalid practice. It is vital to follow prescribed standards to ensure energy efficiency and avoid unnecessary costs.Originality/valueFocus lies in emphasizing the significance of adhering to prescribed standards, such as SBC, to guarantee energy efficiency and prevent unwarranted expenses. Additionally, the authors highlight the crucial role of optimizing glazing properties and allocating the WWR appropriately to achieve energy-efficient building design, accounting for diverse orientations and climatic conditions.
{"title":"The effect of window design factors on the cooling load in hospitals wards","authors":"Abdulbasit Almhafdy, A. Alsehail","doi":"10.1108/sasbe-07-2023-0195","DOIUrl":"https://doi.org/10.1108/sasbe-07-2023-0195","url":null,"abstract":"PurposeThis paper investigates the optimization of window design factors (WDFs) in hospital buildings, particularly in government hospitals within the arid climate of the Qassim region, with the aim of achieving a better cooling load reduction. Continuous monitoring of the hospital ward section is crucial due to patients' needs, requiring optimal indoor air quality and cooling load.Design/methodology/approachThe study identifies the optimal conditions for WDF design to mitigate cooling load, including window-to-wall ratio (WWR), window orientation (WO), room size and U-value (thermal properties), effectively reduce energy consumption in terms of sensible cooling load (MWh/m2) and comply with local codes. Data collection involved a smart weather station, while the Integrated Environmental Solution Virtual Environment (IESVE) software facilitated the simulation process.FindingsKey findings reveal that larger patient rooms were about 40% more energy-efficient than smaller rooms. The northern orientation showed lower energy consumption, and specific WWRs and glazing U-values significantly affected energy loads. In an analysis of U-value changes in energy performance based on the Saudi Building Code (SBC), the presented values did not meet the minimum energy consumption standards. For a valid 40% WWR with a thermal permeability of 2.89, 0.181 MWh/m2 was consumed, while for an invalid 100% WWR with the same permeability but facing the north, 0.156 MWh/m2 was consumed, which is considered an invalid practice. It is vital to follow prescribed standards to ensure energy efficiency and avoid unnecessary costs.Originality/valueFocus lies in emphasizing the significance of adhering to prescribed standards, such as SBC, to guarantee energy efficiency and prevent unwarranted expenses. Additionally, the authors highlight the crucial role of optimizing glazing properties and allocating the WWR appropriately to achieve energy-efficient building design, accounting for diverse orientations and climatic conditions.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1108/sasbe-03-2023-0049
F. Pagliara, Walid El-Ansari, Ilaria Henke
PurposeThe objective of this paper is to propose a methodology to estimate the benefits and costs of stakeholder engagement (SE). Indeed, in the transport sector, it is consolidated that a good decision-making process foresees the involvement of the main stakeholders, but what are the benefits and costs of the SE? How to quantify these impacts and explicitly take them into account in a cost-benefit analysis? In this paper, an attempt to answer these questions is provided.Design/methodology/approachIn this paper, a methodology is proposed to estimate the benefits and costs of SE. Moreover, the proposed methodology is applied to a case study with an attempt to identify direct and indirect cost and benefit drivers within the context.FindingsA range of examples of the monetary costs and benefits of SE is provided through the case study of the high-speed rail corridor connecting Bari and Naples in Italy.Research limitations/implicationsLimits in quantifying all the aspects of engagement.Practical implicationsTo be adopted by public administrations when deciding whether carrying out a project.Social implicationsSocial inclusion is a must in any decision-making process concerning big projects affecting the community.Originality/valueThe original value of this paper is to provide a contribution to the current literature on the quantitative representation of the impacts of SE. Indeed, a methodology to quantify and monetize the costs and benefits of SE is proposed.
{"title":"A methodology to estimate the benefits and costs of stakeholder engagement in a transport decision-making process","authors":"F. Pagliara, Walid El-Ansari, Ilaria Henke","doi":"10.1108/sasbe-03-2023-0049","DOIUrl":"https://doi.org/10.1108/sasbe-03-2023-0049","url":null,"abstract":"PurposeThe objective of this paper is to propose a methodology to estimate the benefits and costs of stakeholder engagement (SE). Indeed, in the transport sector, it is consolidated that a good decision-making process foresees the involvement of the main stakeholders, but what are the benefits and costs of the SE? How to quantify these impacts and explicitly take them into account in a cost-benefit analysis? In this paper, an attempt to answer these questions is provided.Design/methodology/approachIn this paper, a methodology is proposed to estimate the benefits and costs of SE. Moreover, the proposed methodology is applied to a case study with an attempt to identify direct and indirect cost and benefit drivers within the context.FindingsA range of examples of the monetary costs and benefits of SE is provided through the case study of the high-speed rail corridor connecting Bari and Naples in Italy.Research limitations/implicationsLimits in quantifying all the aspects of engagement.Practical implicationsTo be adopted by public administrations when deciding whether carrying out a project.Social implicationsSocial inclusion is a must in any decision-making process concerning big projects affecting the community.Originality/valueThe original value of this paper is to provide a contribution to the current literature on the quantitative representation of the impacts of SE. Indeed, a methodology to quantify and monetize the costs and benefits of SE is proposed.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1108/sasbe-07-2023-0170
Diego Espinosa Gispert, I. Yitmen, Habib Sadri, Afshin Taheri
PurposeThe purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.Design/methodology/approachA scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.FindingsThe research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.Practical implicationsThe proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.Originality/valueThe research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.
{"title":"Development of an ontology-based asset information model for predictive maintenance in building facilities","authors":"Diego Espinosa Gispert, I. Yitmen, Habib Sadri, Afshin Taheri","doi":"10.1108/sasbe-07-2023-0170","DOIUrl":"https://doi.org/10.1108/sasbe-07-2023-0170","url":null,"abstract":"PurposeThe purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.Design/methodology/approachA scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.FindingsThe research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.Practical implicationsThe proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.Originality/valueThe research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138602457","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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