Vol. 4 No. 2 (2026)

Open Access

Article

Article ID: 4056

Decarbonizing precast concrete building components: Cradle-to-site carbon modeling and optimization, explainable machine learning, and a transportation efficiency index

by Peyman Naghipour, Afshin Naghipour, Tarana Bakirova, Hussein Ghiyasi, Faraneh Soltani Gerd Faramarzi, Farazin Soltani Gerd Faramarzi

Building Engineering, Vol.4, No.2, 2026;

Reducing carbon in prefabricated buildings demands component-scale evidence, yet most assessments remain confined to factory production and provide limited, non-transparent guidance on how transportation and on-site installation decisions reshape emissions. This study delivers a consistent framework for quantifying and predicting emissions from the production, transportation, and installation of precast concrete components. It explores the concept that integrating coordinated design standards with logistical planning leads to considerable reductions in cradle-to-site emissions. The framework contributes: (i) a tri-stage system boundary; (ii) a machine-learning plus explainable-AI (XAI) model for transport coupled with a new Transportation Efficiency Index (TEI), defined as delivered component volume-distance per unit CO₂e; and (iii) joint optimization of design standardization and logistics parameters. Empirical data were obtained from a prefabrication plant in Tehran, Iran (156,000 m² footprint; 300,000 m³·yr⁻¹ capacity), including 411 daily energy/resource records, bills of materials and mold-use logs, 408 manufactured components, and matched delivery/installation activities. Gradient-boosted trees yield high predictive accuracy (coefficient of determination R² = 0.99 for production and R² = 0.97 for transportation; mean absolute percentage error MAPE < 6%), while XAI identifies component volume, design standardization, route distance, and truck utilization as dominant drivers; materials account for ~91–98% of production emissions and mold amortization falls from ~9% to <3% when standardization exceeds 0.90 and reuse surpasses ~60 cycles. Scenario optimization improves TEI by ~25% and reduces combined production-to-installation emissions by ~20–30%, providing actionable guidance for manufacturers, contractors, and policymakers seeking low-carbon prefabrication supply chains.

Open Access

Article

Article ID: 4026

An integrated framework for a sustainable hotel complex in Ghazni: A climate-responsive, culturally-attuned model for arid climates

by Mohammad Tahir Zamani, Abdul Saboor Moshwani, Abdullah Khan Kamalzai, Shams-ul-Rahman Faroqzai, Obaid Ullah Sohail Torab, Sayed Hassan Hassan, Ezatullah Popal

Building Engineering, Vol.4, No.2, 2026;

This study addresses the critical need for sustainable hospitality infrastructure in regions with distinct climatic and cultural contexts, focusing on Ghazni, Afghanistan (AFG). It answers two primary research questions: (1) What quantified energy and carbon reductions can be achieved by integrating vernacular passive strategies with active renewable systems in a hotel model for Ghazni? (2) What design parameters ensure cultural relevance, technical feasibility, and local adaptability? The study develops and evaluates an integrated, context-specific Sustainable Hotel Model (SHM) through a mixed-methods approach, combining socio-technical surveys (N = 250), expert interviews (N = 24), and building performance simulation using Autodesk Revit (BIM) and EnergyPlus. A household survey revealed strong public endorsement for sustainability (76% priority) and solar energy (94.95% support), alongside significant gaps in current hotel practices (77.78% perceived no energy efficiency (EE) measures). Expert interviews informed a four-pillar design framework comprising 65 principles across the Socio-Cultural, Economic, Environmental, and Technical domains. Simulation results demonstrate that the proposed SHM achieves a 14.68% reduction in total site energy consumption, a 20.66% reduction in cooling demand, and meets 91.6% of its annual electricity demand via on-site solar photovoltaic (PV) systems. Lifecycle carbon assessment shows a 73.3% reduction in total carbon emissions, driven primarily by an 80.3% reduction in embodied carbon through local, low-embodied-energy materials. The study concludes that authentic sustainability in such contexts requires a synergistic system where high environmental performance is achieved through, not at the expense of, cultural preservation and economic vitality. This research provides a simulation-evaluated, replicable blueprint for decarbonizing the hospitality sector and promoting sustainable regional development in arid, culturally significant regions.

Open Access

Article

Article ID: 3973

Value-based construction management in a housing project: Stakeholder perceptions and project performance in Sudungdewo residence, Indonesia

by Hermawan Hermawan, Rohmatul Aliya, Nurma Khusna Khanifa, Elina Mohd Husini

Building Engineering, Vol.4, No.2, 2026;

This study examines the implementation of value-based construction management principles in the Sudungdewo Residence housing project developed by PT. Kreasi Mandiri Pratama in Wonosobo Regency, Central Java. The research aims to identify how value-based management values are applied in project governance and to evaluate stakeholder perceptions regarding their contribution to project performance. A quantitative descriptive approach was employed using a questionnaire distributed to 20 respondents directly involved in the project, including contractors, consultants, field supervisors, and workers. The assessed principles include justice, transparency, balance, and cooperation, while project performance indicators cover quality, time efficiency, cost efficiency, and sustainability. The results show that respondents generally perceived the application of value-based management principles as positive, reflected in dominant agreement levels across most indicators. High levels of approval were particularly evident in coordination among stakeholders, open communication practices, collaborative problem-solving, balanced consideration of quality–time–cost, and adherence to project standards. However, several items related to cost control, resource efficiency, and long-term community benefits recorded relatively higher neutral responses, indicating that these impacts may not yet be evenly felt or require further strengthening and monitoring. Overall, the study concludes that value-based construction management has been implemented consistently and supports ethical, accountable, and socially responsible project execution, while highlighting the need for improvement in financial efficiency practices and measurable long-term social outcomes.

Open Access

Article

Article ID: 4126

A comparative approach to assess the embodied and operational energy of waste-based masonry materials

by Dulanjali Thoradeniya, Chintha Jayasinghe, Indunil Erandi Ariyaratne

Building Engineering, Vol.4, No.2, 2026;

The construction industry has been recognised as a major contributor to several environmental challenges, mainly due to rapid urbanisation and economic growth that have driven a substantial increase in housing demand. This demand has heavily relied on energy-intensive masonry materials, including cement-sand blocks and fired clay bricks, typically manufactured using depleting natural resources. Consequently, industrial growth often accompanies economic development, resulting in vast quantities of waste, much of which is disposed of in landfills, further exacerbating environmental concerns. In this context, waste-based alternative masonry, including autoclaved aerated concrete (AAC) blocks and expanded polystyrene (EPS) blocks, repurposes industrial waste into building materials, yet lacks empirical energy performance data in tropical climates. This study evaluated and compared them against cement-sand blocks using process-based analysis with work studies conducted at operating manufacturing facilities to evaluate embodied energy. It also employed thermal simulations for a representative middle-income residential unit in Sri Lanka, utilising empirically measured thermal properties of the materials, to compare the operational energy. AAC and EPS walls showed 32–34% higher embodied energy per 1 m² of wall, but yielded 16% and 22% lower annual operational energy, respectively, with annual cooling electricity savings of 37% and 52%. Although the waste-based masonry materials exhibited a higher embodied energy than the conventional reference, the operational energy reductions observed demonstrated clear potential for net savings of energy throughout the lifespan of the buildings. Therefore, waste-based masonry units emerged as viable solutions to reduce total energy consumption in tropical climates and promote circular economy principles.

Open Access

Article

Article ID: 4163

Enhancing the reliability of building crack detection using convolutional neural networks via leveraging robust dataset design

by Sohanur Rahman, Md. Masudur Rahman, Apurba Adikary, Mehedi Hasan Talukder, Minoru W. Yoshida

Building Engineering, Vol.4, No.2, 2026;

The detection of cracks is important in the maintenance of structures like concrete and brick walls, because the appearance of cracks is considered an initial sign of deterioration of structures, ensuring the safety and durability of structures. Traditionally, crack detection is performed by a maintenance engineer manually, which is laborious and time-consuming. Structural maintenance has seen the emergence of automated crack detection methods as a major goal. Convolutional neural network (CNN)-based methods have been superior to other existing methods. But they are not always good in different environments, like shadows, colour changes, or noise, and only work well if the training data is labelled correctly. Thus, CNN-based crack detection requires high-quality labelled datasets. In this research, we assembled comprehensive datasets (captured and online) and employed them in CNN-based techniques (e.g., AlexNet, ResNet-50, GoogLeNet, and VGG16), followed by a comparative analysis to evaluate their performance in structural maintenance. In comparing the performance of the AlexNet, ResNet-50, GoogLeNet, and VGG16 models for crack detection in buildings, ResNet-50 emerged as the top-performing model. All four models achieved high accuracy; however, ResNet-50 consistently demonstrated superior precision, recall, and F1-score. With a test accuracy of 99.88% for ResNet-50, 99.56% for GoogLeNet, 99.25% for VGG16, and 95.31% for AlexNet, ResNet-50 proved more adept at interpreting complex data patterns and minimizing classification errors. This highlights ResNet-50’s stronger ability to enhance classification performance, positioning it as a preferred model for structural crack identification.