The integration of semantic technologies and procedural automation is redefining how infrastructure assets are modeled and managed. Yet, stone-paved roads, common in historical urban centers, remain largely unsupported by existing building information modeling (BIM) standards and tools. This paper presents an innovative, fully automated workflow that bridges this gap by combining a visual programming language (VPL) approach with an ontology-driven semantic framework. Compared to the traditional manual modeling workflow in the BIM-Authoring software Autodesk Civil 3D, which may take several hours, the proposed method generates a complete 3D BIM model by fully automating several modeling operations via a Dynamo graph that takes very short computational times. To achieve this, we developed a parametric modeling pipeline that, based on structured input data, can rapidly generate high-fidelity 3D models of stone-paved roads. To overcome semantic limitations in the Industry Foundation Classes (IFC) schema, we introduce the IFC for stone-paved roads (IFC-SPRO) ontology, designed to enrich geometric models with machine-readable knowledge about modular pavement typologies and maintenance strategies. By converting IFC data into resource description framework (RDF) format and querying it via SPARQL and AI-driven interfaces, the model supports advanced information retrieval for asset management and heritage conservation. The proposed approach not only enhances interoperability and reduces modeling time dramatically but also establishes a scalable foundation for integrating historical road infrastructure into modern digital workflows.

Semantic-Driven Automation of BIM for Stone-Paved Roads: An Ontology-VPL Integrated Approach / Intignano, Mattia; Biancardo, Salvatore Antonio; Guerra De Oliveira, Sara; Tibaut, Andrej; Dell'Acqua, Gianluca. - In: JOURNAL OF COMPUTATIONAL AND COGNITIVE ENGINEERING. - ISSN 2810-9570. - (2025). [10.47852/bonviewjcce52025931]

Semantic-Driven Automation of BIM for Stone-Paved Roads: An Ontology-VPL Integrated Approach

Intignano, Mattia;Biancardo, Salvatore Antonio
;
Dell'Acqua, Gianluca
2025

Abstract

The integration of semantic technologies and procedural automation is redefining how infrastructure assets are modeled and managed. Yet, stone-paved roads, common in historical urban centers, remain largely unsupported by existing building information modeling (BIM) standards and tools. This paper presents an innovative, fully automated workflow that bridges this gap by combining a visual programming language (VPL) approach with an ontology-driven semantic framework. Compared to the traditional manual modeling workflow in the BIM-Authoring software Autodesk Civil 3D, which may take several hours, the proposed method generates a complete 3D BIM model by fully automating several modeling operations via a Dynamo graph that takes very short computational times. To achieve this, we developed a parametric modeling pipeline that, based on structured input data, can rapidly generate high-fidelity 3D models of stone-paved roads. To overcome semantic limitations in the Industry Foundation Classes (IFC) schema, we introduce the IFC for stone-paved roads (IFC-SPRO) ontology, designed to enrich geometric models with machine-readable knowledge about modular pavement typologies and maintenance strategies. By converting IFC data into resource description framework (RDF) format and querying it via SPARQL and AI-driven interfaces, the model supports advanced information retrieval for asset management and heritage conservation. The proposed approach not only enhances interoperability and reduces modeling time dramatically but also establishes a scalable foundation for integrating historical road infrastructure into modern digital workflows.
2025
Semantic-Driven Automation of BIM for Stone-Paved Roads: An Ontology-VPL Integrated Approach / Intignano, Mattia; Biancardo, Salvatore Antonio; Guerra De Oliveira, Sara; Tibaut, Andrej; Dell'Acqua, Gianluca. - In: JOURNAL OF COMPUTATIONAL AND COGNITIVE ENGINEERING. - ISSN 2810-9570. - (2025). [10.47852/bonviewjcce52025931]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/1025718
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