Purpose: The aim of this study is to establish key performance indicators (KPIs) that can be deployed in the evaluation and promotion of safety performance of building developer`s in NigeriaDesign/methodology/approach: A thorough review of the literature was performed to generally identify sets of KPIs used to evaluate the safety performances of building developer`s during construction. Interviews and focus group discussions were conducted with eleven (11) subject matter experts and professionals to ensure inclusion, validation and clarity of the indicators and to further provide agreement/disagreement, and importance ratings for the identified KPIs. The KPIs was grouped into appropriate categories, keeping out redundant KPIs, and ensuring KPIs are clear and measurable. The Relative Importance Index (RII) and Mean values were computed.Findings: Results from the study consist of 137 KPIs, grouped into 9 categories including: Planning, design and procurement, communication on & maintenance of effective safety behaviour, construction safety policy, construction safety personnel, management effort and support, safety training and enlightenment, administration of safety processes, investigation and reporting of accidents, and rewards and sanctions for project stakeholders. These KPIs were observed to be at different levels of importance from the respondents. The following KPIs were considered based on their extreme importance judging from the respective RII values: communicating safety requirements to designer (98.18%), safety performance set as part of contractor selection criterion (96.36%), availability and accessibility of the relevant insurance policies (98.18%) and appropriate issuance of motivational directives by the top management (100%).Originality/value: Many studies have been done in the past where KPIs as it relates to construction safety were identified. However, the specificity of these KPIs to countries other than Nigeria requires similar research be conducted to identify building developer safety performance KPIs for the Nigerian construction industry.

The increasing complexity of urban building energy systems necessitates advanced prediction methods for efficient energy management. Urban buildings account for approximately 40% of global energy consumption, making accurate prediction crucial for sustainability goals. This research develops a novel transformer-based deep learning framework for multi-building energy consumption forecasting. Despite recent advances in energy prediction techniques, existing models struggle with multi-building scenarios due to limited ability to capture cross-building correlations, inadequate integration of diverse data streams, and poor scalability when deployed at urban scale—gaps this research specifically addresses. The study implemented a modified transformer architecture with hierarchical attention mechanisms, processing data from 100 commercial buildings across three climate zones over three years (2020–2023). The framework incorporated weather parameters, occupancy patterns, and historical energy consumption data through multi-head attention layers, employing a 4000-step warm-up period and adaptive regularization techniques. The evaluation included a comparison with the baseline models (ARIMA, LSTM, GRU) and robustness testing. The framework achieved a 23.7% improvement in prediction accuracy compared to traditional methods, with a mean absolute percentage error of 3.2%. Performance remained stable across building types, with office complexes showing the highest accuracy (MAPE = 2.8%) and healthcare facilities showing acceptable variance (MAPE = 3.5%). The model-maintained prediction stability under severe data perturbations while demonstrating near-linear computational scaling. The transformer-based approach significantly enhances building energy prediction capabilities, enabling more effective demand-side management strategies, though future research should address long-term adaptability.

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

There are strong indications that the built environment has had a great influence on the course of the COVID-19 pandemic and the post-disaster recovery. The COVID-19 pandemic has adversely affected both human and global development, while efforts to combat this menace call for an integrated human social capital index. This research seeks to enhance understanding of how the built environment can be enhanced through resilience against the backdrop of the COVID-19 pandemic. This study aims to investigate the impact of a resilient built environment on increasing resilience in the aftermath of the COVID-19 pandemic in Nigeria. The quantitative studies test the impact of four built environment resilience indices (built environment capital, disaster management indices, awareness of the COVID-19 pandemic, and built environment adaptive strategies) on human social capital and COVID-19 pandemic indices. This study reveals the role of human social capital in achieving a resilient built environment in the wake of the COVID-19 pandemic in Nigeria. Built environment capital, disaster management indices, and awareness of COVID-19 also indirectly affect the COVID-19 pandemic indices through human social capital. This study’s implications are useful for post-COVID-19 recovery, which is important for future planning of the built environment in Nigeria.