Assessing lift maintenance performance of high-rise residential buildings
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Cited by 3 publications
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Transforming high-rise residential maintenance: a 3D spatio-temporal model utilizing Industry 4.0 and lean principles
Purpose The purpose of this paper is to develop and demonstrate a comprehensive 3D spatio-temporal maintenance management model for high-rise residential buildings by integrating Industry 4.0 technologies and lean maintenance principles. This model aims to optimize maintenance scheduling, enhance resource utilization and improve decision-making processes. By leveraging advanced data visualization and predictive analytics, this study seeks to address the complexities of building maintenance, ensure timely interventions, reduce downtime and extend the lifespan of building assets, ultimately leading to more efficient and sustainable maintenance management practices. Design/methodology/approach Integrating state-of-the-art technologies such as big data analytics and artificial intelligence into the proposed model is geared towards benefiting from optimized maintenance scheduling and resource allocation, hence achieving minimum asset downtime and extension in asset life. This is being done through the digitization of paper maps, the development of 3D building models in AutoCAD and SketchUp and the placing of the developed models into ArcGIS Pro. The PostgreSQL database with PostGIS extension supports optimal storage and management of spatial data towards real-time updates and advanced analyses. Findings The results revealed that the model enhances maintenance planning considerably better than traditional methods due to the revelation of meaningful patterns and trends that are not visible in conventional visualization methods. Temporal analysis indicates increasing needs for maintenance through time, whereas spatial analysis can point out the units that require special attention. The spatiotemporal analysis is needed to determine overall maintenance requirements for better decision-making. The work demonstrated that 3D visualization of maintenance activities performed over building representation helps facility managers in better decision-making related to task planning for performance improvement concerning building and tenant satisfaction. Research limitations/implications The study’s current limitations include the reliance on specific datasets and technologies, which may need adaptation for broader applications. Future research could explore further integration with additional building types and longitudinal studies to assess long-term impacts. Practical implications The 3D visualization of maintenance activities over building representation aids facility managers in better decision-making related to task planning, improving building performance and tenant satisfaction. This integrated approach provides significant benefits in efficiency, resource use and sustainability. Originality/value The originality of this paper lies in its innovative integration of 3D spatio-temporal data with Industry 4.0 technologies and lean maintenance principles to create a comprehensive maintenance management model for high-rise residential buildings. Unlike traditional approaches, this model combines advanced data visualization, real-time analytics and predictive maintenance strategies within a unified geographic information system framework. This holistic approach not only enhances maintenance planning and resource allocation but also provides a proactive, data-driven methodology that significantly improves the efficiency and effectiveness of maintenance management, addressing the unique challenges of high-rise residential building maintenance.
Transforming high-rise residential maintenance: a 3D spatio-temporal model utilizing Industry 4.0 and lean principles
Purpose The purpose of this paper is to develop and demonstrate a comprehensive 3D spatio-temporal maintenance management model for high-rise residential buildings by integrating Industry 4.0 technologies and lean maintenance principles. This model aims to optimize maintenance scheduling, enhance resource utilization and improve decision-making processes. By leveraging advanced data visualization and predictive analytics, this study seeks to address the complexities of building maintenance, ensure timely interventions, reduce downtime and extend the lifespan of building assets, ultimately leading to more efficient and sustainable maintenance management practices. Design/methodology/approach Integrating state-of-the-art technologies such as big data analytics and artificial intelligence into the proposed model is geared towards benefiting from optimized maintenance scheduling and resource allocation, hence achieving minimum asset downtime and extension in asset life. This is being done through the digitization of paper maps, the development of 3D building models in AutoCAD and SketchUp and the placing of the developed models into ArcGIS Pro. The PostgreSQL database with PostGIS extension supports optimal storage and management of spatial data towards real-time updates and advanced analyses. Findings The results revealed that the model enhances maintenance planning considerably better than traditional methods due to the revelation of meaningful patterns and trends that are not visible in conventional visualization methods. Temporal analysis indicates increasing needs for maintenance through time, whereas spatial analysis can point out the units that require special attention. The spatiotemporal analysis is needed to determine overall maintenance requirements for better decision-making. The work demonstrated that 3D visualization of maintenance activities performed over building representation helps facility managers in better decision-making related to task planning for performance improvement concerning building and tenant satisfaction. Research limitations/implications The study’s current limitations include the reliance on specific datasets and technologies, which may need adaptation for broader applications. Future research could explore further integration with additional building types and longitudinal studies to assess long-term impacts. Practical implications The 3D visualization of maintenance activities over building representation aids facility managers in better decision-making related to task planning, improving building performance and tenant satisfaction. This integrated approach provides significant benefits in efficiency, resource use and sustainability. Originality/value The originality of this paper lies in its innovative integration of 3D spatio-temporal data with Industry 4.0 technologies and lean maintenance principles to create a comprehensive maintenance management model for high-rise residential buildings. Unlike traditional approaches, this model combines advanced data visualization, real-time analytics and predictive maintenance strategies within a unified geographic information system framework. This holistic approach not only enhances maintenance planning and resource allocation but also provides a proactive, data-driven methodology that significantly improves the efficiency and effectiveness of maintenance management, addressing the unique challenges of high-rise residential building maintenance.
Unveiling buyers’ preferences for privately developed affordable housing: a study in Kolkata, India
Purpose The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences. Design/methodology/approach The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses. Findings The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance. Originality/value This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.
Comparative Review of Lift Maintenance Regulations in Beijing, Hong Kong, and London
Lifts, or elevators, are transportation facilities that are indispensable for countless end users in high-rise buildings. They require proper maintenance to ensure safe operation. In addition to technological applications, effective management and legislative controls play a crucial role in ensuring lift safety. Given the limited understanding of an optimal regulatory model for governing lift maintenance, a cross-discipline comparative study was conducted to examine lift maintenance regulations in regions with different legal systems. Following a systematic and comparative review approach, this study focused on regulatory controls across civil and common law jurisdictions, specifically Beijing, Hong Kong, and London. Relevant statutes and publications were searched from engineering, law and management databases, which included Scopus, JSTOR, Lexis+, Lexis China, Lexis Advance Hong Kong, and Westlaw Asia. Through scrutinizing the retrieved documents, key features of the regulations were identified and compared in terms of lift classifications, types and frequencies of mandatory maintenance works, qualifications for authorized parties, and legal liabilities for non-compliance. Validated by industry experts, the results reveal both similarities and differences in the regulations among the three jurisdictions. While these findings serve as valuable references for policymakers in formulating optimal legislative controls to enhance lift safety in the future, further research could expand the scope of this study to examine the regulations in other regions and investigate the effectiveness of existing statutory controls on lift maintenance.
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