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Record W4412754729 · doi:10.11159/iccste25.330

Towards Sustainable Construction: A Simulation-Driven Evaluation of a 3D-Printed Building for DGNB Certification

2025· article· en· W4412754729 on OpenAlexvenueno aff
Shahad AlJabri, Azza Al-Balushi, Yasmine Souissi

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCertification3d printedComputer scienceArchitectural engineeringConstruction engineeringConstruction industrySystems engineeringManufacturing engineeringEngineering managementEngineeringManagement

Abstract

fetched live from OpenAlex

Green Buildings (GB) as a pathway to sustainability have emerged as a key solution to address the environmental impact of buildings throughout their lifecycle.GB certification systems offer a performance framework to achieve sustainability based on pre-assumed parameters during the construction and operation phases.This paper examines a case study of the German Sustainable Building Council (DGNB)'s New Construction International system as a certification body for a 3Dprinted research building.The key towards evaluating certification criteria includes the three sustainability pillars: environmental, economic, and sociocultural quality [1].Multiple simulation software programs were used to assess different aspects of building design to verify certification prior to construction initiation.Thermal simulation utilized DesignBuilder integrated with EnergyPlus to assess envelope performance, specifically cooling demand under hot and arid conditions.Visual comfort and accessibility were evaluated through DIAlux Evo with scene-specific lighting simulation and ISO 21542 compliance walkthroughs, verifying principles of inclusive design being matched [2].A Life Cycle Costing (LCC) analysis was performed using an interconnected Excel tool.Calculating the cost of construction, whole life cost, and maintenance projections introspective of the economic conditions of the region were also part of this study.Project results provide an evaluation of holistic building potential to be DGNB certified, suggesting optimization strategies based on results to ensure performance improvement across all required categories [3].Calculated results reveal enhanced performance in thermal comfort with 0 unmatched cooling hours achieved, indicating optimal indoor temperatures.Adaptive comfort standards revealed through simulation alignment with ASHARE 55 confirming high occupant satisfaction potential [4].Analysis conducted on Computational Fluid Dynamics (CFD) confirmed efficient air circulation with improvements recommended for solar shading along exposed faades.Muscat's Extreme summers show peak cooling demands through energy simulation reports with the projected Energy Usage Intensity (EUI) being 355.77kWh/m^2/year.Visual comfort simulation showed compliance with uniformity and emergency lighting requirements for each building zone.Initial LCC revealed that 71% of the cost stem from initial construction procedure.Renewable energy integration reduces the long-term operational costs net impact by 42% enhancing overall economic feasibility.Assessment towards material procurement found that over 95% of construction components were locally sourced, minimising transport emissions according to DGNB.The 3D printed concrete mix utilised incorporates CEMEX D.fab additives that reduce embodied carbon to 320 CO eq/m for the same standard mortar volume [5].This demonstrates the value of simulation-based planning towards DGNB certification in hot climate zones like Oman Moreover, the project aligns with the United Nations Sustainable Development Goals (SDGs) [6] and supports the strategic objectives of Oman Vision 2040 [7], particularly in promoting sustainable urban development, innovation in construction technologies, and responsible resource management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.264
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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