Automatic Identification and Geometrical Modeling of Steel Rivets of Historical Structures from Lidar Data

Abstract

Riveting is a joining technique that was widely used in iron and steel structures from the mid-nineteenth to the early twentieth century. Nowadays, many of these riveted structures are still in service around the world, and in many instances, also constitute an important part of the built cultural heritage. The maintenance and conservation of this type of construction is a crucial task for which the HBIM (Historic Building Information Modeling) methodology has recently gained increased attention, postulating itself as the ideal tool for tracking control and conservation-related actions. In the process of data collection and 3D modeling of the structure, the rivets are an important part to be taken into account in the structural safety assessment and health monitoring over time. Any structure of this typology typically presents thousands of rivets, so its measurement and subsequent 3D geometrical modeling is a laborious task and a source of possible errors. Accordingly, this work presents a novel methodology that allows the automatic identification and 3D modeling of rivets in iron and steel structures from LIDAR data. The proposed methodology has been tested with both laboratory specimens and a full-scale real bridge.

Framework for BIM-Based Repair History Management for Architectural Heritage

“…They have also examined its benefits and drawbacks in improving maintenance and operational efficiency. Furthermore, studies have investigated the incorporation of cutting-edge technologies and equipment, such as laser scanners, photogrammetry, and visual programming, into HBIM [19][20][21][22]. In the realm of the facility management of heritage buildings, there have been studies on data utilization methods to support decision making for building maintenance.…”

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

“…Omer et al [47] also applied TLS for the purpose of constructing the DT model to examine the condition of concrete bridge structures and employed virtual reality (VR) for the inspection of bridges within an immersive 3D virtual environment. Similar studies were also carried out using TLS concerning the development of an as-built model for structural assessment of bridges, which could be used as a reliable basis for 3D model generation of bridge DTs [48][49][50].…”

Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure

“…The use of ML and DL algorithms in the 3D surveying of built heritage is a rapidly growing field. Recent research in the domains of cultural heritage [14,23] and land surveying [24] has demonstrated that AI algorithms can be applied to analyze and interpret data collected from sensors and can process large amounts of 3D information in a more automated manner as well as identify features, structural and architectural components [25,26], damage mechanisms [16], or materials [27] that manual methods might miss.…”

“…A supervised ML algorithm, the random forest (RF) [28], generally used for imagebased classification, has recently been applied for the semantic segmentation of 3D data to assist in different applications such as monitoring [29] and restoration [23], and even to support the scan-to-BIM process. Since the first studies by Weinmann [30] and Hackel [31] on geometry features that can be extracted from the point cloud, RF has been applied to support activities such as degradation analysis [21], the detection of architectural components and urban-scale objects [32], and the recognition of specific features of painting or sculpture.…”