Said Quqa

Said Quqa
University of Bologna | UNIBO · Department of Civil, Chemical, Environmental and Materials Engineering DICAM

PhD fellow

About

28
Publications
5,680
Reads
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115
Citations
Introduction
Ph.D. student at University of Bologna with a structural engineering background. He contributes to research in Structural Health Monitoring (SHM), with insights into technical aspects related to smart wireless sensor networks and output-only modal identification algorithms.
Additional affiliations
November 2018 - present
University of Bologna
Position
  • PhD Student
Education
September 2011 - March 2017
University of Bologna
Field of study
  • Architecture and Building Engineering

Publications

Publications (28)
Article
Full-text available
One of the latest trends in structural health monitoring involves the use of wireless decentralized sensing systems, developed to reduce costs and speed up the whole monitoring process. The main purpose of this paper is to present a novel decentralized procedure for the instantaneous modal identification of time-varying structures, also suitable in...
Article
Full-text available
Most time–frequency representations (TFRs) and signal analysis methods used for the identification of dynamic systems through non-parametric techniques are based on univariate signals. However, combining the information obtained from different sensors to investigate the overall behavior of the monitored structure is not trivial, as different record...
Article
Full-text available
Thanks to emerging techniques in the field of signal processing and due to improvements in smart sensing systems which enable the event‐triggered acquisition of high‐fidelity data at the occurrence of strong ground motion events, seismic structural health monitoring has grown considerably in the last few decades. In this paper, the modal assurance...
Article
Full-text available
Widespread monitoring of bridges is yet rarely employed at a territorial level due to the high costs of monitoring systems. However, the aging of civil infrastructures, combined with the growing traffic demand, poses the need for a simple and automatic tool that helps emergency management. In this paper, an integrated algorithm for the identificati...
Article
Full-text available
Recent research in Indirect Structural Health Monitoring (ISHM) uses the dynamic response of instrumented vehicles to carry out “drive-by” monitoring of bridges. These vehicles are generally cars or trucks instrumented with different types of sensors. However, some urban bridges are inaccessible to regular vehicles. Also, cars and trucks have non-n...
Chapter
Due to growing traffic demand, aging civil infrastructure raises the need for reliable tools to monitor structural health conditions, usable to plan informed maintenance and emergency management. Several structures with historical and monumental importance are instrumented with structural health monitoring (SHM) systems nowadays. However, even the...
Article
Full-text available
Detecting early damage in civil structures is highly desirable. In the area of vibration-based damage detection, modal flexibility (MF)-based methods have proven to be promising tools for promptly identifying changes in the global structural behavior. Many of these methods have been developed for specific types of structures, giving rise to differe...
Article
Full-text available
Pressure mapping has garnered considerable interest in the healthcare and robotic industries. Low-cost and large-area compliant devices, as well as fast and effective computational algorithms, have been proposed in the last few years to facilitate distributed pressure sensing. One approach is to use electrical impedance tomography (EIT) to reconstr...
Article
Smart devices for structural health monitoring provide edge computing capabilities to reduce wireless transmission and, thus, power consumption. Although effective algorithms have been proposed in the last few decades, traditional microcontrollers require heavy data flow between the memory and the central processing unit that involves a considerabl...
Article
Full-text available
In the aftermath of a seismic event, decision-makers have to decide quickly among alternative management actions with limited knowledge on the actual health condition of buildings. Each choice entails different direct and indirect consequences. For example, if a building sustains low damage in the mainshock but people are not evacuated, casualties...
Article
Aging structural components, together with the increasing transportation needs and limited budgets, are challenging aspects that typically concern decision-makers and infrastructure owners. Although Structural Health Monitoring (SHM) has been a powerful tool to optimize maintenance-related activities and post-disaster emergency management, the sens...
Preprint
Full-text available
Smart devices for structural health monitoring provide edge computing capabilities to reduce wireless transmission and, thus, power consumption. Although effective algorithms have been proposed in the last few decades, traditional microcontrollers require heavy data flow between the memory and the central processing unit that involves a considerabl...
Article
Full-text available
The advent of parallel computing capabilities, further boosted through the exploitation of graphics processing units, has resulted in the surge of new, previously infeasible, algorithmic schemes for structural health monitoring (SHM) tasks, such as the use of convolutional neural networks (CNNs) for vision-based SHM. This work proposes a novel appr...
Article
Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or subst...
Article
Full-text available
In this paper, a new strategy for vibration-based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented struct...
Chapter
Full-text available
Based on the variety of methods available for gathering data for the aircraft health status, the challenge is to reduce the overall amount of data in a trackable and safe manner to ensure that the remaining data are characteristic of the current aircraft status. This chapter will cover available data reduction strategies for this task and discuss t...
Chapter
Full-text available
This chapter aimed to present different data driven Vibration-Based Methods (VBMs) for Structural Health Monitoring (SHM). This family of methods, widely used for engineering applications, present several advantages for damage identification applications. First, VBMs provide continuous information on the health state of the structure at a global le...
Chapter
In civil engineering structures it is highly desirable to detect the presence of damage and changes in the global structural behavior at the earliest possible stage, and, among the many existing strategies for vibration-based damage detection, modal flexibility (MF)-based approaches are promising tools. However, in most of the existing studies, the...
Conference Paper
Full-text available
Traditional algorithms for the identification of dynamic parameters based on vibrational structural response generally involve strict assumptions about stationarity and may not be suitable for time-varying systems. Therefore, there is a tendency to discard the measurements of the structural response collected during short-time events, such as train...
Article
This paper proposes a novel method suitable for vibration-based damage identification of civil structures and infrastructures under ambient excitation. The damage-sensitive feature employed in the presented algorithm consists of a vector of multivariate autoregressive parameters estimated from the vibration responses collected at different location...
Article
Full-text available
Recent developments in the field of smart sensing systems enable performing simple onboard operations which are increasingly used for the decentralization of complex procedures in the context of vibration-based structural health monitoring (SHM). Vibration data collected by multiple sensors are traditionally used to identify damage-sensitive featur...
Article
Full-text available
One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unli...
Chapter
Real-time structural health monitoring (SHM) acquires countless importance when applied to large-scale civil infrastructures, where the damage should be managed immediately to avoid both economic and human loss. Recent studies in the field of real-time identification of bridges generally assume linear time-varying (LTV) structural models, justified...
Conference Paper
Structural health monitoring (SHM) includes a wide range of methods focusing on the improvement of structural reliability and life cycle management of engineered systems. In the past decades, smart maintenance procedures have been proposed and implemented for a large number of civil structures with strategic and monumental relevance. Nowadays, rapi...
Article
Full-text available
The main purpose of this work is to investigate the usability of easily obtainable parameters instead of the modal traditional ones, in the context of a flexibility-based damage detection procedure, under the assumption of unknown structural masses. To this aim, a comparison is made between two different approaches: the first involves the calculati...

Questions

Questions (2)
Question
I am trying to extract features from a nonstationary signal (frequencies and time locations). For this purpose, I computed a wavelet transform (complex morlet) of the signal in MATLAB and obtained the scalogram as in the picture attached. Than I tried to decompose the scalogram matrix by SVD, in order to localize the peaks and the spreads of energy in the image. The problem is that every matrix obtained from the decomposition (one for every singular value) contains informations about more than one single atom (peak in the image). I've read many articles about rotating the SVD basis in order to decompose the original matrix in matrices connected each to a single atom, but I'm not able to apply it in practice. If we consider the standard SVD:
A=U*S*transpose(V)
a source in particular [Bashor & Kareem, Efficacy of Time-Frequency Domain System Identification Scheme Using Transformed Singular Value Decomposition] talks about a new SVD given by:
A=Y*Z*transpose(X)
where Y, Z, and X are the transformed singular vector and singular value matrices, defined as:
Y=U*C;
X=V*D;
Z=transpose(C)*S*D;
and the trasformation matrices are found by:
E[Y]=transpose(C)*M*C
"where C are the eigenvectors of M that maximize the mean and M is a matrix of the singular vectors". My problem is to understand the meaning of the latter sentence and to apply it in MATLAB.
Question
I tried to follow the theory described in many articles about the system identification and feature detection for a nonstationary signal. Many of them refears to SVD to detect ridges and extract features (bandwidth and time location), but some explain how to rotate the SVD basis in order to estimate a more precise location, by enforcing the first moments of crossdensity function to be zero.
I tried to compile a MATLAB script, but the features do not refer to the single atom of the density distribution. I'm not understanding if there is a problem in the script or the spectrogram generated by 'wspectrogram' is not adequate. How could I define in MATLAB the transformation matrices?

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Cited By

Projects

Projects (5)
Project
Civil structures and infrastructure are continuously subjected to natural hazards, including seismic activity, flooding, and wind, which may cause degradation of the structural performance and even lead to collapse. Currently, decisions related to maintenance and emergency management mainly consist of heuristic methods relying on the results of visual inspections and expert opinion. In recent years, infrastructure asset managers have been promoting permanent static and dynamic monitoring systems. Information acquired by these systems, such as loads and environmental actions, structural performance, and deterioration level, can reduce uncertainties in estimating the serviceability level of structures. With this information, asset agencies may implement optimal structural management decisions guaranteeing an adequate level of structural safety. Although structural health monitoring (SHM) has been gaining increasing interest in the research community, several aspects need further investigation. This Special Issue invites high-quality contributions addressing the current state of the art, recent developments, and future perspectives in SHM in hazardous environments. The topics of interest include but are not limited to: Monitoring under hydro-geological hazard, e.g., considering flood, scour, and landslides; Seismic SHM; The influence of soil-structure interaction in SHM; Emerging technologies for monitoring structures, infrastructures, and ground motion, e.g., unmanned aerial vehicles (UAV) and satellites; Informed structural management under multiple natural hazards. The authors can contribute by presenting both theoretical papers and case studies. Detailed information on the special issue is available at: https://www.mdpi.com/journal/geosciences/special_issues/civil_SHM
Project
The advent of parallel computing, further boosted by last-generation graphics processing units, has resulted in the surge of new, previously infeasible algorithmic schemes for structural health monitoring (SHM). However, most algorithms employed nowadays for this purpose belong to the field of supervised learning, and thus, they require a training dataset. This project comprehends the results of research activities conducted to simplify, interpret, and apply machine learning tools consciously for damage identification in civil structures. This research also deals with the challenges related to the creation of training datasets suitable for real applications.
Project
Dense sensor networks are expensive and unsuitable for monitoring many bridges and viaducts on a vast territory. However, exploiting moving load can lead to accurate identification of dense structural features using sparse low-cost sensor networks. In this project, the influence lines are extracted by examining the quasi-static component of vibration data collected during the passage of vehicles. These features contain valuable information on the state of health of the instrumented civil infrastructure.