Usman Mehmood

Purpose: High-rise residential buildings face persistent maintenance inefficiencies due to reactive workflows, poor spatiotemporal integration, and suboptimal resource allocation. Current approaches inadequately address the complexities of vertical structures and dynamic maintenance requirements, creating critical gaps in proactive management. This study aims to bridge these gaps by developing and validating a 3D GIS-integrated rule-based scheduling model, enhancing proactive decision-making, resource optimisation, and regulatory compliance. Design/methodology/approach: A 3D GIS-integrated rule-based scheduling model was developed, combining spatial visualisation, temporal analytics and Unified Modeling Language (UML) logic. A dataset of 4,070 maintenance work orders was collected from three high-rise residential buildings managed by a Malaysian university Asset Management Department (2020–2024). Data were digitised, modeled in SketchUp, integrated via ArcGIS Pro and PostgreSQL/PostGIS, and validated against manual scheduling. Findings: The model reduced delays in high-priority tasks by 17%, improved compliance with task urgency by 23%, and achieved 100% technician utilisation. It also supported spatiotemporal fault visualisation, trend detection, and workload balancing. Lean maintenance principles and life cycle cost analysis were embedded, ensuring alignment with Malaysia’s Strata Management Act 2013. Practical implications: The model offers facility managers, developers, and Management Corporation a scalable, data-driven tool for improving scheduling efficiency and regulatory compliance in high-rise maintenance operations. Originality/value: This study uniquely integrates 3D Geographic Information Systems, temporal analytics, and rule-based logic into a unified scheduling framework. It moves beyond conventional systems by enabling dynamic prioritisation, proactive planning, and sustainability-focused maintenance. The model serves as a foundation for further integration with Internet of Things monitoring and smart city systems.

Purpose: This study aims to identify and quantify the principal determinants of maintenance costs in higher education buildings using a multi-model econometric approach. It integrates operational, structural and climatic variables to provide generalizable insights for cost-effective and climate-responsive facility management (FM) across institutions. Design/methodology/approach: The analysis draws on 1.9 million maintenance work orders from 25 North American universities (2002–2021). A combination of mixed-effects modeling, quantile regression and panel data analysis is employed to examine how institutional, operational and environmental factors jointly influence maintenance expenditure. This multi-method econometric framework captures cost heterogeneity, handles nested data structures and distinguishes between cross-sectional and longitudinal effects. Findings: Planned Preventive Maintenance significantly reduces costs by up to 57% in high-expenditure cases (75th percentile) compared with unplanned maintenance. Labor remains the largest cost driver, while better building conditions consistently lower expenses. Climatic variables show mixed impacts: precipitation amplifies costs during prolonged maintenance tasks, whereas temperature variability, humidity and wind chill often exert weak or negative effects. Originality/value: The study extends the existing literature by applying a comprehensive econometric framework to a large, multi-institutional dataset, one of the most extensive of its kind in the higher education sector. By bridging quantitative modeling with FM practice, it offers empirical evidence to support predictive budgeting, resource optimization and climate-adaptive maintenance planning.

Purpose: This study aims to investigate how national climate policy enforcement influences environmental, social and governance (ESG) performance in green building startups. While ESG has been widely studied in large corporations, its relationship with regulatory enforcement in innovation-driven green building startups remains underexplored. Design/methodology/approach: The study constructs Climate Policy Enforcement Index (CPEI) using eight regulatory enforcement components sourced from the World Bank. Firm-level ESG scores are derived using ESG-BERT, a natural language processing (NLP) model. A cross-sectional dataset of 858 green building startups across 63 countries is analyzed using multivariate fixed-effects regression, with heterogeneity explored by ESG subdimensions, firm revenue group and enforcement component. Findings: Stronger climate policy enforcement is positively associated with overall ESG performance, particularly in the environmental dimension. Mid-sized startups show the highest responsiveness, while high-revenue firms appear decoupled from enforcement signals. Not all regulatory mechanisms are equally effective; early-stage enforcement tools such as energy code compliance and permitting processes have the most robust impact. Conversely, disclosure mandates and retrofit obligations may generate unintended negative effects. Originality/value: This study advances ESG and institutional theory by highlighting the differentiated impact of enforcement mechanisms on startup behavior. It introduces an enforcement index tailored to the built environment and applies a state-of-the-art NLP model to assess ESG performance on a scale. The results offer actionable insights for policymakers designing sustainability regulation and investors evaluating ESG risk in early-stage firms.

The Land Administration Domain Model (LADM) standardizes land management by integrating legal, spatial, and administrative information. This study examines LADM-related research using Structural Topic Modelling (STM) on 199 publications (2008-2024). Seven dominant topics emerged: land administration systems, property valuation, 3D cadastral modelling, LADM extensions, building and spatial rights, cadastral systems, and land object modelling. Key findings highlight sustained interest in spatial modelling, legal frameworks, and cadastral data integration, alongside emerging trends such as country-specific LADM profiles (e.g., China, Kenya, Malaysia) and technological advancements like BIM and marine georegulation models. Challenges persist in data complexity, semantic interoperability, and 4D cadastres. The study recommends expanding semantic models, fostering interdisciplinary collaboration, and developing tailored national profiles to enhance LADM’s applicability and promote sustainable land management practices globally.

With the rise of high-rise residential buildings in urban areas, effective maintenance management is increasingly crucial. This study introduces a 3D-STBMM model that integrates 3D visualization and temporal analysis to enhance maintenance management in Malaysian high-rises. The model aims to improve decision-making, problem-solving, and resource utilization. Using digital reconstructions of maps and maintenance records from January 2020 to January 2024, the study uncovers patterns in the timing and frequency of civil and electrical maintenance activities. Civil tasks peaked in 2022 before declining, while electrical tasks showed a consistent pattern with a decrease in 2023. Spatial analysis highlights the prevalence of these activities across maintenance spread and frequency. The integrated model demonstrates potential to transform maintenance practices, enabling a data-driven, strategic approach that enhances the performance and sustainability of urban high-rise buildings.

Urbanization has sparked an increase in the construction of multi-use highrise buildings which consists of commercial parcels on their lower floors and residential parcels on their higher floors. In contrast to conventional landed houses, the residents of high-rise buildings share common facilities and private parcels or spaces also differ according to ownership or use. The management and maintenance of these spaces are dependent on the ownership of the parcel where each ownership adheres to different rights, restrictions, and responsibilities (RRRs). Therefore, accurate representation and identification of those parcels affected by maintenance or renovation is crucial for assisting management bodies to improve the quality of life within a multi-use high-rise building. This study attempts to implement a temporal maintenance management for highrise building parcels within a 3D spatial database. A 3D space segmentation was done to analyze the ownership and use of space in a high-rise building. Spatial queries were also performed based on the temporal maintenance of the parcels; in addition, 3D spatial relationships were used to determine adjacent parcels that were affected by the maintenance. Thus, the implementation of temporal strata database management with an accurate 3D representation of the space can provide management bodies with concise and comprehensive information on parcels with respect to ownerships and uses.

Due to urban regions' lack of land for housing and infrastructure development, multi-level (vertical) constructions have developed rapidly. Massive developments and integration of high-rise buildings and structures, such as multi-story houses and apartments, are now prevalent in Malaysia. When it comes to the housing industry, preventive maintenance is critical because it helps individuals maintain their daily routines while also enhancing the productivity of activities in and around their residences. Therefore, the primary focus of this research is to review the temporal strata maintenance issues, build a conceptual domain model for maintenance procedures of residential strata in Malaysia, and link it with existing Land Administration Domain Model (LADM) packages. Furthermore, a Unified Modeling Language (UML) model for this LADM extension was created and is presented here on a conceptual level. This conceptual domain model for maintaining high-rise residential strata is designed by identifying the actors, classes, and their relationships derived from literature review, then created in the Enterprise Architect software. This conceptual domain model can further be used to develop maintenance management databases and software, which are nowadays essential for effective strata maintenance management.