D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Development of BIM-based Automated Methods for Building Management and Structural Safety Assessment

Despite the progress made in modern project management methods, there is still a lack of appropriate automated tools that support digital integration over the project life cycle. There is considerable demand for fully embracing the latest technological opportunities such as Building Information Modeling (BIM), Internet of Things (IoT), Structural Health Monitoring (SHM), and prefabrication to support that digital transformation in construction. The aim of this study is to develop a set of automated management solutions and related tools to address the issues highlighted above. The thesis is presented as a collection of manuscripts of five peer-reviewed journal articles authored based on the present research. The first development is of a BIM-based method for 3D model visualization of buildings and their non-structural elements and their corresponding seismic risk levels and locations. It supports automated assessment of seismic risk of these elements. The second focuses on the development of a novel data-driven SHM technique to monitor the structural behavior of individual building modules to detect possible damages during their transportation. It consists of two main components, a sensor-based data acquisition (DAQ) and storage module, and an automated data analysis module that uses unsupervised machine learning techniques to identify damages during transportation using onboard captured acceleration data. It can be used to ascertain the safety of delivered modules before their assembly on site. The third accounts for the development of an automated BIM-based framework to facilitate effective data management and representation of sensory components of the SHM tool used in buildings. It allows for visualization of damages in building components based on the interpretation of the captured sensor data. It is designed to facilitate effective visualization capabilities for a rapid and efficient structural condition assessment. The fourth development is designed to dynamically update the thermal comfort data in monitored buildings by integrating their BIM models with captured sensor data. The default range utilized in this development is based on the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Standard. It is expected to provide a robust and practical tool for data collection, analysis, and visualization to facilitate intelligent monitoring of the thermal condition in buildings and help decision-makers take needed timely data-driven decisions. The fifth and last development is designed to alert IoT companies of malfunctioning of deployed sensors utilizing a BIM platform and a cloud database to process and transfer related actionable information

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Digital workflows for the management of existing structures in the pre- and post-earthquake phases: BIM, CDE, drones, laser-scanning and AI

La metodologia BIM, sviluppata in America negli anni '70, ha rivoluzionato l'industria delle costruzioni introducendo i principi di innovazione e digitalizzazione per la gestione dei progetti, in un settore settore produttivo troppo legato a logiche tradizionali. I numerosi processi digitali che sono stati sviluppati da allora hanno riguardato in gran parte la progettazione di nuovi edifici, e sono principalmente legati alla disciplina del construction management. Alcune prime sperimentazioni condotte nel tempo hanno mostrato come l'estensione di questa metodologia agli edifici esistenti comporti molte difficoltà. In questo panorama, il lavoro di tesi si concentra sulla gestione delle strutture nella fase pre e post-sisma con l'obiettivo di sviluppare processi digitali basati sull'uso di tecnologie innovative applicate sia agli edifici ordinari che a quelli storici. Il primo workflow sviluppato, relativo alla fase pre-sisma, è stato denominato scan-to-FEM, ed è finalizzato a particolarizzare il classico processo scan-to-BIM nel campo dell'ingegneria strutturale, analizzando così tutti i passaggi dal rilievo dell'edificio con le tecniche digitali di fotogrammetria e laser-scanning fino all'analisi strutturale e alla valutazione della sicurezza nei confronti delle azioni sismiche. I processi di gestione delle strutture post-sisma sono invece incentrati sulla stima della sicurezza della struttura e sulla definizione delle strategie di intervento, e si basano sull'analisi delle caratteristiche intrinseche della struttura e dei danni indotti dagli eventi sismici. L'intero processo di valutazione del livello operativo di un edificio è stato quindi rivisto alla luce delle moderne tecnologie digitali. Nel dettaglio, sono state sviluppate Reti Neurali Convoluzionali (CNN) per la crack detection, e l'estrazione delle informazioni numeriche associate alle lesioni, gestite poi grazie ai modelli BIM. I quadri fessurativi sono stati digitalizzati grazie allìintroduzione un nuovo oggetto BIM "lesione" (attualmente non codificato nello standard IFC), al quale è stato aggiunto un set di parametri in parte valutati con le CNN ed in parte qualitativi. Durante lo sviluppo di questi processi, sono stati sviluppati nuovi strumenti adhoc per la gestione degli edifici esistenti. In particolare, sono state definite specifiche per lo sviluppo di schede tecniche digitali dei danni, e per la creazione del nuovo oggetto BIM "lesione". I processi di gestione degli edifici danneggiati, grazie agli sviluppi tecnologici realizzati, sono stati applicati per la digitalizzazione dell'edificio storico della chiesa di San Pietro in Vinculis danneggiato a seguito di eventi sismici, grazie ai quali sono stati sperimentati i massimi benefici in termini di riduzione di tempo e risparmio di risorse

Vision-Based Control of Unmanned Aerial Vehicles for Automated Structural Monitoring and Geo-Structural Analysis of Civil Infrastructure Systems

The emergence of wireless sensors capable of sensing, embedded computing, and wireless communication has provided an affordable means of monitoring large-scale civil infrastructure systems with ease. To date, the majority of the existing monitoring systems, including those based on wireless sensors, are stationary with measurement nodes installed without an intention for relocation later. Many monitoring applications involving structural and geotechnical systems require a high density of sensors to provide sufficient spatial resolution to their assessment of system performance. While wireless sensors have made high density monitoring systems possible, an alternative approach would be to empower the mobility of the sensors themselves to transform wireless sensor networks (WSNs) into mobile sensor networks (MSNs). In doing so, many benefits would be derived including reducing the total number of sensors needed while introducing the ability to learn from the data obtained to improve the location of sensors installed. One approach to achieving MSNs is to integrate the use of unmanned aerial vehicles (UAVs) into the monitoring application. UAV-based MSNs have the potential to transform current monitoring practices by improving the speed and quality of data collected while reducing overall system costs. The efforts of this study have been chiefly focused upon using autonomous UAVs to deploy, operate, and reconfigure MSNs in a fully autonomous manner for field monitoring of civil infrastructure systems. This study aims to overcome two main challenges pertaining to UAV-enabled wireless monitoring: the need for high-precision localization methods for outdoor UAV navigation and facilitating modes of direct interaction between UAVs and their built or natural environments. A vision-aided UAV positioning algorithm is first introduced to augment traditional inertial sensing techniques to enhance the ability of UAVs to accurately localize themselves in a civil infrastructure system for placement of wireless sensors. Multi-resolution fiducial markers indicating sensor placement locations are applied to the surface of a structure, serving as navigation guides and precision landing targets for a UAV carrying a wireless sensor. Visual-inertial fusion is implemented via a discrete-time Kalman filter to further increase the robustness of the relative position estimation algorithm resulting in localization accuracies of 10 cm or smaller. The precision landing of UAVs that allows the MSN topology change is validated on a simple beam with the UAV-based MSN collecting ambient response data for extraction of global mode shapes of the structure. The work also explores the integration of a magnetic gripper with a UAV to drop defined weights from an elevation to provide a high energy seismic source for MSNs engaged in seismic monitoring applications. Leveraging tailored visual detection and precise position control techniques for UAVs, the work illustrates the ability of UAVs to—in a repeated and autonomous fashion—deploy wireless geophones and to introduce an impulsive seismic source for in situ shear wave velocity profiling using the spectral analysis of surface waves (SASW) method. The dispersion curve of the shear wave profile of the geotechnical system is shown nearly equal between the autonomous UAV-based MSN architecture and that taken by a traditional wired and manually operated SASW data collection system. The developments and proof-of-concept systems advanced in this study will extend the body of knowledge of robot-deployed MSN with the hope of extending the capabilities of monitoring systems while eradicating the need for human interventions in their design and use.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169980/1/zhh_1.pd

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Tunnel Engineering

This volume presents a selection of chapters covering a wide range of tunneling engineering topics. The scope was to present reviews of established methods and new approaches in construction practice and in digital technology tools like building information modeling. The book is divided in four sections dealing with geological aspects of tunneling, analysis and design, new challenges in tunnel construction, and tunneling in the digital era. Topics from site investigation and rock mass failure mechanisms, analysis and design approaches, and innovations in tunnel construction through digital tools are covered in 10 chapters. The references provided will be useful for further reading

Safe and Sound: Proceedings of the 27th Annual International Conference on Auditory Display

Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022), June 24-27. Online virtual conference

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). It gives an overview of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to understanding and reducing landslide disaster risk