Frontal geometry from sketches of engineering objects: is line labelling necessary?

A tool which can quickly interpret line drawings (with hidden lines removed) of engineering objects as boundary representation CAD models would be of significant benefit in the process of engineering design. Inflation of the drawing to produce a frontal geometry, a geometric realisation of that part of the object visible in the drawing, is an important stage of this process. Previous methods of producing frontal geometries have relied on the technique of line labelling (labelling edges as convex, concave or occluding). Although restricted subsets of the line-labelling problem have known solutions, reliable methods have not been found for the general line-labelling problem, and traditional methods, when adapted to drawings with non-trihedral junctions, are unacceptably slow. Many other papers assume that line labelling is an essential step. Here, we show this is not necessarily true, and that comparable results can be obtained by a novel alternative approach. Firstly, we consider what outputs from line labelling are essential to the production of frontal geometry. Secondly, we investigate by what other means these outputs can be produced. Our work indicates that the only essential output from line labelling for frontal geometry is the determination of which T-junctions in a drawing are occluding and which are non-occluding. This information is required for inflation, and also for detection of symmetry and for constructing hidden topology. Thus, we propose and analyse a new method which, in the absence of line labels, simultaneously inflates a drawing to produce the frontal geometry and attempts to determine whether each T-junction is occluding or not. For drawings of objects with holes or pockets, and for cases where line labelling is particularly unreliable, our new method can provide a better alternative

Optimization of survey procedures and application of integrated digital tools for seismic risk mitigation of cultural heritage: The Emilia-Romagna damaged theatres.

Starting from current procedures, standards and tools for seismic damage survey, the research presents an integrated workflow for seismic damage documentation and survey applied to historic theatres in the Emilia-Romagna region damaged by the 2012 earthquake. The 2012 earthquake highlighted the fragility of the cultural heritage and underscored the lack of proactive conservation and management of historic assets. The research starts by analysing Agenzia Regionale per la Ricostruzione della Regione Emilia-Romagna- ARRER’s requests, which had found criticalities in applying the current Mic (Ministero della Cultura) procedures for the damage survey of complex types: the A-DC form for churches and the B-DP form for buildings. Using the two types of forms highlighted the lack of ad hoc tools for complex architectural styles such as castles, cemeteries and theatres, resulting in the loss of quantitative and qualitative information necessary for knowledge, conservation and thus management of the reconstruction process. As a result of these considerations, national and international standards of integrated documentation, existing digital databases for cataloguing and classification of cultural property, and seismic risk management were studied to develop a workflow of integrated procedures for seismic damage survey on the specific assigned case study: Regional Historic Theaters affected by the 2012 earthquake. The research used the holistic and interdisciplinary approach of integrated documentation to develop the integrated procedural workflow to enhance and optimise seismic damage detection operations in the case study. In providing a workflow of integrated procedures for the prevention and mitigation of hazards related to potential states of emergency, both natural and anthropic, the research follows an “extensive” methodological approach to test the survey outside the Emilia crater. The methodological framework led to the critical-comparative analysis, divided into two levels: the first involved studying critical issues in the B-DP form, mainly used in the 2012 theatre survey. The second level covered the techniques - laser scanning, digital photogrammetry - and integrated survey methodologies applied during the in-depth investigations for repair and restoration work. The critical-comparative analysis and morpho-typological study led to the development of an integrated procedural flow to survey damage in historic theatres. It is aimed at systematising and optimising the stages of damage documentation. The workflow consists of three information levels: L1. Screening level for the visual survey; L2 survey level defines the 3D acquisition steps for the geometric-dimensional study by theatres. The BIM L3 Plus level guides implementing the level of knowledge of parametric HBIM models for documentation, management and monitoring of historic theatres

Automatic creation of boundary-representation models from single line drawings

This thesis presents methods for the automatic creation of boundary-representation models of polyhedral objects from single line drawings depicting the objects. This topic is important in that automated interpretation of freehand sketches would remove a bottleneck in current engineering design methods. The thesis does not consider conversion of freehand sketches to line drawings or methods which require manual intervention or multiple drawings. The thesis contains a number of novel contributions to the art of machine interpretation of line drawings. Line labelling has been extended by cataloguing the possible tetrahedral junctions and by development of heuristics aimed at selecting a preferred labelling from many possible. The ”bundling” method of grouping probably-parallel lines, and the use of feature detection to detect and classify hole loops, are both believed to be original. The junction-line-pair formalisation which translates the problem of depth estimation into a system of linear equations is new. Treating topological reconstruction as a tree-search is not only a new approach but tackles a problem which has not been fully investigated in previous work

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements