Data-Driven Values in Data-Driven Architecture Design; Validation of the D2V Model

Naderi Delpak, Shahla; Shahcheraghi, Azadeh; Zarabadi, Zahra Sadat Saeideh

doi:10.22034/ijumes.2026.2096545.1388

Data-Driven Values in Data-Driven Architecture Design; Validation of the D2V Model

Document Type : Original Article

Authors

Shahla Naderi Delpak

Azadeh Shahcheraghi

Zahra Sadat Saeideh Zarabadi

Department of Architecture, SR.C, Islamic Azad University, Tehran, Iran

10.22034/ijumes.2026.2096545.1388

Abstract

The advent of big data in the architecture, engineering and construction industry has shifted the role of information from a purely representational instrument to a generative driver of the design process. Nevertheless, a deep operational gap persists between abstract data analytics and the physical realization of spatial relationships in architecture. This study introduces the notion of “data-driven value” as a mediating layer intended to bridge this gap, and proposes a conceptual model for translating structured physical and behavioral data into spatial qualities. The study introduces the D2V model, a framework for the conceptual integration of semantic-web technologies with space-syntax logic. To validate the model’s components, a three-round Delphi panel of fifteen experts in architecture, urban design, spatial analysis, building information modelling and data science was convened. The Content Validity Ratio and Content Validity Index were computed for every component. The results indicate that “data-driven value” is realized when data act upon the spatial logic of the building and thereby enhance legibility, functional performance and prediction accuracy. The Delphi panel confirmed that all D2V components possess satisfactory content validity. The components “prediction accuracy” and “spatial legibility” attracted the greatest expert consensus, and Kendall’s coefficient of concordance reached 0.85 by the third round, indicating strong agreement among the experts. The proposed D2V model redefines the architect’s role from “author of form” to “designer of semantic systems.” By realizing data-driven value, architects can reduce ambiguity in design and ensure that data-driven methodologies produce function-oriented, human-centered environments rather than mere geometric complexity.

Graphical Abstract

Data-Driven Values in Data-Driven Architecture Design; Validation of the D2V Model

Highlights

· The study introduces “data-driven value” as a mediating concept that bridges the operational gap between abstract data analytics and the physical realization of spatial relationships in architecture.

· The proposed D2V (Data-to-Value) model integrates semantic-web ontologies (e.g., ifcOWL) with space-syntax logic across three layers: data ontology, semantic translation and configuration engine, and value realization.

· A three-round Delphi panel of expert practitioners and researchers confirmed the content validity of all model components (CVR ≥ 0.42 and CVI ≥ 0.78; overall S-CVI/Ave = 0.82).

· Expert consensus was strongest for “prediction accuracy” (CVI = 0.85) and “spatial legibility” (CVI = 0.83), and Kendall’s coefficient of concordance rose to 0.85 (P < 0.05) by the third round.

· The model reframes the architect as a “designer of semantic systems,” reducing design ambiguity and ensuring that data-driven methods yield function-oriented, human-centered space rather than mere geometric complexity.

Keywords

Big data

D2V model

Data-driven value

Ontology

Sspace syntax

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