Perspective Chapter: Expert Opinions on Data-Driven LCA-Based Frameworks for Modular Façade Systems

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Март 14, 2025

This study explores the potential, challenges, and perspectives of data-driven frameworks to enhance environmental impact assessment promote circular economy practices across life cycle modular façade systems. Building on prior research innovation efforts in field, semi-structured interviews were conducted with stakeholders representing diverse backgrounds ensure a wide range perspectives, including health, safety, environment (HSE) managers, engineers, software developers. The methodology was organised into three main phases. It began preparation phase, followed by conduct interviews. During these interviews, previous findings presented, leading discussion centred perceived related technologies. Finally, thematic analysis identify common insights that emerged. emphasise importance standardised data models, interoperability between systems, role value chain networking collaboration advancing practices. While not exhaustive, provides valuable evolving landscape sustainable construction offers recommendations for future developments LCA-based Frameworks integration.

Язык: Английский

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A machine-learning model for estimating construction renovation costs

International Journal of Building Pathology and Adaptation, Год журнала: 2025, Номер unknown

Опубликована: Апрель 19, 2025

Purpose A serious concern for construction costs has been the presence of uncertainties in operations and how they affect project performance. Several models exist predicting costs. However, overlook effects on This study, therefore, aims to develop a predictive model that considers uncertainty when estimating building renovation Design/methodology/approach The study employed scope factors 45 development. SHapley Additive exPlanations (SHAP) was used reveal had significant impact improve performance model. then outcome sensitivity analysis along with train test prediction using XGBoost. Findings found crude oil price, complexity, delays payment, regulatory requirements Inappropriate design have most XGBoost produced promising outcomes an accuracy 91.20%. Practical implications from this will enable managers stakeholders make informed decisions, optimise resource allocation mitigate risks. Originality/value To cost projects, it is essential take into account, its predictions value predictions. In novel machine learning approach developed predict projects by leveraging factors.

Язык: Английский

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Modelling the driving forces of green retrofitting adoption in developing nations: A data-driven approach

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112921 - 112921

Опубликована: Май 1, 2025

Язык: Английский

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