It is stated that 3D recording and modeling of heritage buildings entail accurate building models (as-built). However, this paper presents an analysis of the 3D modeling accuracy for the creation of historical building information models (HBIM), considering the complexity and the deformations of historical buildings, using point cloud data and BIM tools. The 3D modeling processes analyzed are based on a three-stage semi-automatic approach leading to the generation of HBIM, including manual and automatic processes. The three stages consist of: (a) optical and terrestrial laser scanning; (b) meshing processes; and finally (c) 3D solid modeling to be assembled into HBIM. Next, this approach analyzed the mesh deformations generated automatically in comparison to the initial point cloud data. The deformations and the accuracy evaluation have been undertaken using different commercial software. Finally, our modeling approach shows that it can improve the accuracy of the 3D models achieved using existing BIM technologies.

Terrestrial laser scanning is a widely used technology to digitise archaeological, architectural and cultural heritage. This allows for modelling the assets’ real condition in comparison with traditional data acquisition methods. This paper, based on the case study of the basilica in the Baelo Claudia archaeological ensemble (Tarifa, Spain), justifies the need of accurate heritage modelling against excessively simplified approaches in order to support structural safety analysis. To do this, after validating the 3D meshing process from point cloud data, the semi-automatic digital reconstitution of the basilica columns is performed. Next, a geometric analysis is conducted to calculate the structural alterations of the columns. In order to determine the structural performance, focusing both on the accuracy and suitability of the geometric models, static and modal analyses are carried out by means of the finite element method (FEM) on three different models for the most unfavourable column in terms of structural damage: (1) as-built (2) simplified and (3) ideal model without deformations. Finally, the outcomes show that the as-built modelling enhances the conservation status analysis of the 3D heritage city (in terms of realistic compliance factor values), although further automation still needs to be implemented in the modelling process.

Metro accidents are apt to cause serious consequences, such as casualties or heavy economic loss. Once accidents occur, quick and accurate decision-making is essential to prevent emergent accidents from getting worse, which remains a challenge due to the lack of efficient knowledge representation and retrieval. In this research, an ontological method that integrates case-based reasoning (CBR) and natural language processing (NLP) techniques was proposed for metro accident case retrieval. An ontological model was developed to formalize the representation of metro accident knowledge, and then, the CBR aimed to retrieve similar past cases for supporting decision-making after the accident cases were annotated by the NLP technique. Rule-based reasoning (RBR), as a complementary of CBR, was used to decide the appropriate measures based on those that are recorded in regulations, such as emergency plans. A total of 120 metro accident cases were extracted from the safety monthly reports during metro operations and then built into the case library. The proposed method was tested in MyCBR and evaluated by expert reviews, which had an average precision of 91%.