Sugiarto Sugiarto
Universitas Sains dan Teknologi Komputer, Semarang, Indonesia

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Integrating Climate-Resilient Design And Life Cycle Costing In Green Building Projects: A Simulation-Based Assessment In Tropical Urban Areas Samsul Arifin; Angga Setiyadi; Purwanto Purwanto; Sugiarto Sugiarto
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3cq4x038

Abstract

Tropical urban areas are increasingly exposed to the compounded impacts of climate change, including rising temperatures, high humidity, and increased rainfall, which pose challenges to the long-term performance, durability, and cost-efficiency of green buildings. This study integrates climate-resilient building design strategies with Life Cycle Costing (LCC) to evaluate both the technical performance and long-term economic feasibility of green building projects in tropical urban environments. A simulation-based building performance assessment was conducted to model key microclimatic variables, solar radiation, thermal loads, and precipitation, and their impacts on building envelope performance, passive cooling strategies, and water management systems. Simulation outputs were incorporated into an LCC framework to compare multiple design scenarios over a 30-year operational lifecycle. The results indicate that climate-resilient design alternatives reduce annual building energy demand by approximately 15–25% and lower total life-cycle costs by 10–18% compared to baseline green-building configurations, despite an initial capital cost increase of 5–12%. These findings demonstrate that investments in climate-adaptive strategies enhance long-term cost efficiency, operational stability, and resilience to extreme climate conditions in tropical cities. This study provides a coherent simulation-based framework that links environmental performance analysis with life-cycle economic evaluation, offering practical decision-support insights for architects, engineers, developers, and policymakers. By quantitatively revealing trade-offs between initial investment and long-term benefits, the research addresses a critical gap in current green building assessment practices and supports the development of financially viable and climate-resilient urban building solutions.
Integrating Predictive AI Models to Bridge Energy Efficiency Gaps in Smart Building Design Sugiarto Sugiarto; Liam Christopher; Amelia Grace
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/2fwp7m63

Abstract

Energy efficiency has become one of the most important aspects in smart building design, especially considering the gap that has increasingly been noted between simulated energy performance and actual consumption. Even though digital design tools like BIM have enhanced design capabilities, there is still a big gap in energy performance, usually rooted in the static nature of traditional simulations. This research tries to respond to this challenge by proposing a conceptual framework linking predictive AI models with BIM for enhanced accuracy in early design stage forecasting. Other than a few studies that revolved around optimization in the post-occupancy phase, this study applies a conceptual-simulative methodology by using a synthetic BIM model of a medium-sized office building. Machine learning algorithms, such as random forest and gradient boosting, were trained on parameterized design data for predicting EUI. Strong predictive consistency was identified with an R² of 0.89 between the predicted and simulated EUI and a conceptual reduction of the performance gap of about 18%. The model also shows robust logical correspondence to the concepts of energy efficiency within a wide range of design scenarios. This research concludes that predictive AI can significantly improve energy performance forecasting in smart building design and provides a proactive data-driven approach toward overcoming the energy efficiency gap in support of more sustainable architectural practices without immediate physical field testing.