Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Procedural Historic Building Information Modelling (HBIM) is a new approach for modelling historic buildings which develops full building information models from remotely sensed data. HBIM consists of a novel library of reusable parametric objects, based on historic architectural data and a system for mapping these library objects to survey data. Using concepts from procedural modelling, a new set of rules and algorithms have been developed to automatically combine HBIM library objects and generate different building arrangements by altering parameters. This is a semi-automatic process where the required building structure and objects are first automatically generated and then refined to match survey data.
The encoding of architectural rules and proportions into procedural modelling rules helps to reduce the amount of further manual editing that is required. The ability to transfer survey data such as building footprints or cut-sections directly into a procedural modelling rule also greatly reduces the amount of further editing required. These capabilities of procedural modelling enable a more automated and efficient overall workflow for reconstructing BIM geometry from point cloud data. This document outlines the research carried out to evaluate the suitability of a procedural modelling approach for improving the process of reconstructing building geometry from point clouds. To test this hypothesis, three procedural modelling prototypes were designed and implemented for BIM software. Quantitative accuracy testing and qualitative end-user scenario testing methods were used to evaluate the research hypothesis. The results obtained indicate that procedural modelling has potential for achieving more accurate, automated and easier generation of BIM geometry from point clouds.
Dore, C. (2017) Procedural Historic Building Information Modelling (HBIM) For Recording and Documenting European Classical Architecture. Doctoral thesis, DIT, 2017.