Contemporary architecture, as well as design, has revolutionised the approach to form creation, prioritising increasingly efficient and, above all, adaptive modelling. It is essential for designers to identify the requirements that the product to be realised must meet for its own production. This methodology, driven by technological advancements in the field of artificial intelligence (AI) and the use of advanced algorithms, enables the exploration and generation of optimised products from various perspectives based on chosen criteria; the resulting forms integrate functionality and aesthetics. This research proposes the use of tools within the parametric modelling environment Grasshopper, such as Galapagos and Octopus, which employ AI algorithms to enhance the design process and optimise forms for additive manufacturing. Experimentation with these plugins allows leveraging evolutionary algorithms to explore a wide range of design solutions, enabling designers to efficiently optimise complex forms. In this context, AI facilitates tackling multi-objective optimisation problems, improving parameters such as structural strength, material usage, and minimisation of printing times. This approach not only enhances the efficiency of the design process but also opens up new possibilities for innovation in design by integrating the advanced computational capabilities of AI with the creative potential of parametric design.
Semi-Automated Feature Recognition and Localization in 3D Point Cloud by Using AI
The application of Artificial Intelligence (AI) in Cultural Heritage surveying has gained notable interest, particularly in enhancing the classification and segmentation of 3D point clouds. Recent research focuses on automating processes to manage surveying data and integrating it into the BIM environment. The present research proposes a semi-automated AI-based pipeline to semantically classify architectural features in 3D point clouds and match them with an existing BIM library. The case study examines the school and theatre of the Crespi d’Adda industrial village, a UNESCO Cultural Heritage site. Data includes a 3D point cloud generated via terrestrial photogrammetry, with windows selected as the feature to model in the BIM library. The methodology encompasses three phases: (i) creating a Crespi d’Adda window dataset aligning with BIM library parameters; (ii) applying Machine Learning classifiers for semantic categorization; and (iii) using prediction algorithms to recognize windows in the point cloud, matching them to the BIM library, and calculating accuracy. This research bridges architectural representation and data mining, streamlining BIM reconstruction. Anticipated results include automated detection and labeling of elements with accurate placement in the BIM environment, enhancing efficiency and interdisciplinary integration.
AI for Archaeological Heritage Applications
Lo studio propone una metodologia basata sull’integrazione di algoritmi di intelligenza artificiale e algoritmi generativi per supportare la ricostruzione di manufatti archeologici frammentari. Attraverso tecniche di image generation (GAN, Transformer), semantic segmentation e modelli computazionali parametrici, il sistema consente di analizzare frammenti, riconoscerne l’appartenenza tipologica e generare ipotesi ricostruttive tridimensionali. L’approccio viene validato tramite un workflow iterativo che combina dataset visivi, classificazione geometrica e ottimizzazione tramite algoritmi genetici, con verifica finale da parte di esperti.
REWIND: Interactive Cognitive Artefacts for Lost Landmarks Rediscovery
This paper presents a methodological framework for rediscovering lost urban landmarks through web-based interactive cognitive artefacts that combine geolocation, 3D modelling, historical reconstruction, and gamified user interaction. The research focuses on the majolica domes of Naples, once distinctive elements of the historic skyline that were progressively transformed, hidden, or demolished through urban growth, earthquakes, and restoration interventions. Starting from archival documents, historical maps, iconographic sources, and survey data, the authors generate digital twins and level-of-information-oriented 3D models able to visualize changes across time. These models are then integrated into interactive platforms such as Google tools, CAD Mapper, Autodesk Infraworks, and panoramic tour systems, allowing users to compare past and present urban landscapes both remotely and in situ. The selected case study of the Church of SS. Apostoli demonstrates how users can virtually rewind time to observe successive transformations of the dome, including the loss of its lantern and majolica cladding. The research highlights the potential of low-cost digital tools, AR/VR applications, and immersive storytelling to strengthen cultural awareness, public participation, and the perception of urban identity.
Genetic Algorithms for Polycentric Curves Interpretation
This research work into Evolutionary Computing field aims at improving a dataset of algorithmic gen-erative definitions able to return an optimized ‘semi–ideal’ curve that best fits a generic reality–based profile, starting from some of its points. This paper shows GAs_Genetic Algorithms applications especially with regards to study, interpretation and definition of generic polycentric curves. Current VPL tools (Galapagos–Rhino, McNeel) allow to test Evolutionary Theories for problem solving and decision makingin architectural research field. According to a human driven approach, an operator defines GENOME, FUNCTION and FITNESS to drive the Evolutionary Solver towards optimized solutions. Some case studies from Historical/Existent Architectural Heritage are used to show how GAs can simplify the digitalization process and big data interpretation.