Construction Technologies and Architecture Vol. 22

Abstract: This study assesses the impact and challenges of Building Automation Systems (BAS) in five selected estates within Abuja Municipal Area Council (AMAC), Nigeria. Using a descriptive design, purposive sampling targeted 115 apartments across the estates. Data from 127 valid responses were analyzed using SPSS, ANOVA, and regression models. Findings reveal a significant relationship between BAS implementation and improved energy efficiency and security (β = 0.314, p < 0.001), supported by BAS awareness (β = 0.288, p = 0.001). Joint participation (β = 0.457), safety (β = 0.446), and resident satisfaction (β = 0.322) significantly shaped stakeholder perceptions, while maintenance (p = 0.136) and information availability (p = 0.256) were less impactful. Over 60% of respondents cited high costs, inadequate technical skills, and outdated infrastructure as major barriers. The study recommends stakeholder engagement, capacity building, and infrastructure upgrades to support effective BAS adoption for sustainable estate management.

Abstract: The need and quest for sustainable road infrastructure demands alternative, cost-effective and readily available tools that reduce environmental impacts during asphalt mix design. This study examines the application of Excel-based regression modelling as a cost-effective Artificial Intelligence tool to predict the optimum bitumen content in asphaltic concrete mixtures. Traditional mix design methods are resource-intensive and carbon-heavy, particularly in low-income countries. This study utilizes a dataset collected from six geopolitical zones in Nigeria and applies Multiple Linear Regression via Microsoft Excel to develop a predictive model. The model was calibrated and validated using standard error indices and physical lab tests. Results showed that the Excel-based model reduced the need for full-scale experimental mixes by over 90%, achieving an R² of 0.996 and a standard error of less than 0.35. This method significantly reduces material waste, emissions, and energy consumption. The study positions Excel-based regression modelling as a practical AI-enabled tool for engineers in resource-constrained environments. Future research should explore the integration of Excel with other add-in tools and the incorporation of real-time climate and traffic variables.