Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

OPERATIONAL MODEL ANALYSIS AND FINITE ELEMENT MODEL UPDATE USING AMBIENT VIBRATION DATA FOR AL-SINYAR TOWER

Buildings in Qatar rely on minimum structural code requirements implemented by design consultants’ offices. Qatar 2030 vision considers increasing of structures’ sustainability and serviceability as a high priority, which require testing structures under real full scale modeling. The process of monitoring structures’ behavior over time for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). In Qatar, most high-rise building stability design is based on wind loading. According to Uniform Building Code3 1997 (UBC1997) which classifies seismic zones on a scale of zero to four, Qatar’s seismic classification on the scale is zero which is the minimum seismic risk value. Qatar Meteorological data on wind speeds enabled analysis of extreme winds to be undertaken in structural designs. This study aims to identify dynamic properties of the structural by using wired and wireless accelerometers in order to assess structural performance to update Finite Element Model (FEM). By updating FEM, engineers are enabled to support clients to make quick and correct decisions in extreme emergency situations in the case of boundary conditions changes and loads such as seismic vibration and wind pressure changes, during a structure’s life. The objective of this research is to apply and evaluate a single output-only procedure on a reinforced concrete tower building, Al Sinyar Tower, which consists of 2B+G+52 floors in Al Dafna Area in Qatar, with a total built up area of 74,747 sqm and is the tallest residential building in Qatar with a total height of 230 m . A Finite Element model using Sap2000 program was used to model and analyze building values in order to compare results with the real test results. The different forms of response data from ambient vibration were scrutinized to evaluate structure performance. Mode shapes, natural frequencies, modal damping ratios were studied, while the results of tests carried under ambient conditions were used to update the Finite Element model based on modules of elasticity, density and also connections fixity. The thesis concluded that wired sensors are not practical to use for low frequencies measurements in high rise buildings and that it is tremendously challenging and difficult to deal with more than 1000 meter long cables, especially with a very sensitive devices. Frequencies values from wired sensors could not been captured, whereas wireless connection provided more reasonable values. Ambient vibration results based on as-built environment provided higher frequency values in comparison to FEM because the stiffness provided by cladding, façade and walls eventually increased the system’s stiffness, which cannot be revealed in FEM based on structural drawings only. The foremost concept of Model Updating is to have an ideal simulation of structure that can represent real structure behavior. The Final Updated model results founded satisfactory according to modal assurance criterion (MAC) value with 98.9% and frequency deference errors average of 7.6%

Multisensory Smartphone Applications in Vibration-Based Structural Health Monitoring

Advances in sensor technology and computer science in the last three decades have boosted the importance of system identification and vibration-based structural health monitoring (SHM) in civil infrastructure safety and integrity assessment. On the other hand, practical and financial issues in system instrumentation, maintenance, and operation have remained as fundamental problems obstructing the widespread use of SHM applications. For this reason, to reduce system costs and improve practicality as well as sustainability, researchers have been working on emerging methods such as wireless, distributed, mobile, remote, smart, multisensory, and heterogeneous sensing systems. Smartphones with built-in batteries, processor units, and a variety of sensors, have stood as a promising hardware and software environment that can be used as SHM components. Communication capabilities with the web, enable them to compose a smart and participatory sensor network of outnumbered individuals. Besides, crowdsourcing power offered by citizens, sets a decentralized and self-governing SHM framework which can even be pertained by very limited equipment and labor resources. Yet, citizen engagement in an SHM framework brings numerous challenges as well as opportunities. In a citizen-induced SHM scenario, the system administrators have limited or no control over the sensor instrumentation and the operation schedule, and the acquired data is subjected change depending on the measurement conditions. The citizen-induced errors can stem from spatial, temporal, and directional uncertainties since the sensor configuration relies on smartphone users’ decisions and actions. Moreover, the sensor-structure coupling may be unavailable where the smartphone is carried by the user, and as a consequence, the vibration features measured by smartphones can be modified due to the human biomechanical system. In addition, in contrast with the conventional high fidelity sensors, smartphone sensors are of limited quality and are subjected to high noise levels. This dissertation utilizes multisensory smartphone features to solve citizen-induced uncertainties and develops a smartphone-based SHM methodology which enables a cyber-physical system through mobile crowdsourcing. Using smartphone computational and communicational power, combined with a variety of embedded sensors such as accelerometer, gyroscope, magnetometer and camera, spatiotemporal and biomechanical citizen-induced uncertainties can be eliminated from the crowdsourced smartphone data, and eventually, structural vibrations collected from numerous buildings and bridges can be collected on a single cloud server. Therefore, unlike the conventional platforms designed and implemented for a particular structure, citizen-engaged and smartphone-based SHM can serve as intelligent, scalable, fully autonomous, cost-free, and durable cyber-physical systems drastically changing the forthcoming trends in civil infrastructure monitoring. In this dissertation, iOS is used as the application development platform to produce a smartphone-based SHM prototype, namely Citizen Sensors for SHM. In addition, a web-based software is developed and cloud services are implemented to connect individual smartphones to an administrator base and automate data submission and processing procedure accordingly. Finally, solutions to citizen-induced problems are provided through numerous laboratory and field test applications to prove the feasibility of smartphone-based SHM with real life examples. Through collaborative use of the software, principles and methodologies presented in this dissertation, smartphones can be the core component of futuristic smart, resilient, and sustainable city and infrastructure systems. And this study lays down an innovative and integrated foundation empowering citizens to achieve these goals

Enabling smart city resilience: Post-disaster response and structural health monitoring

The concept of Smart Cities has been introduced to categorize a vast area of activities to enhance the quality of life of citizens. A central feature of these activities is the pervasive use of Information and Communication Technologies (ICT), helping cities to make better use of limited resources. Indeed, the ASCE Vision for Civil Engineering in 2025 (ASCE 2007) portends a future in which engineers will rely on and leverage real-time access to a living database, sensors, diagnostic tools, and other advanced technologies to ensure that informed decisions are made. However, these advances in technology take place against a backdrop of the deterioration of infrastructure, in addition to natural and human-made disasters. Moreover, recent events constantly remind us of the tremendous devastation that natural and human-made disasters can wreak on society. As such, emergency response procedures and resilience are among the crucial dimensions of any Smart City plan. The U.S. Department of Homeland Security (DHS) has recently launched plans to invest $50 million to develop cutting-edge emergency response technologies for Smart Cities. Furthermore, after significant disasters have taken place, it is imperative that emergency facilities and evacuation routes, including bridges and highways, be assessed for safety. The objective of this research is to provide a new framework that uses commercial off-the-shelf (COTS) devices such as smartphones, digital cameras, and unmanned aerial vehicles to enhance the functionality of Smart Cities, especially with respect to emergency response and civil infrastructure monitoring/assessment. To achieve this objective, this research focuses on post-disaster victim localization and assessment, first responder tracking and event localization, and vision-based structural monitoring/assessment, including the use of unmanned aerial vehicles (UAVs). This research constitutes a significant step toward the realization of Smart City Resilience.National Science Foundation Grant No. 1030454Association of American RailroadsOpe

Interval and Fuzzy Computing in Neural Network for System Identification Problems

Increase of population and growing of societal and commercial activities with limited land available in a modern city leads to construction up of tall/high-rise buildings. As such, it is important to investigate about the health of the structure after the occurrence of manmade or natural disasters such as earthquakes etc. A direct mathematical expression for parametric study or system identification of these structures is not always possible. Actually System Identification (SI) problems are inverse vibration problems consisting of coupled linear or non-linear differential equations that depend upon the physics of the system. It is also not always possible to get the solutions for these problems by classical methods. Few researchers have used different methods to solve the above mentioned problems. But difficulties are faced very often while finding solution to these problems because inverse problem generally gives non-unique parameter estimates. To overcome these difficulties alternate soft computing techniques such as Artificial Neural Networks (ANNs) are being used by various researchers to handle the above SI problems. It is worth mentioning that traditional neural network methods have inherent advantage because it can model the experimental data (input and output) where good mathematical model is not available. Moreover, inverse problems have been solved by other researchers for deterministic cases only. But while performing experiments it is always not possible to get the data exactly in crisp form. There may be some errors that are due to involvement of human or experiment. Accordingly, those data may actually be in uncertain form and corresponding methodologies need to be developed. It is an important issue about dealing with variables, parameters or data with uncertain value. There are three classes of uncertain models, which are probabilistic, fuzzy and interval. Recently, fuzzy theory and interval analysis are becoming powerful tools for many applications in recent decades. It is known that interval and fuzzy computations are themselves very complex to handle. Having these in mind one has to develop efficient computational models and algorithms very carefully to handle these uncertain problems. As said above, in general we may not obtain the corresponding input and output values (experimental) exactly or in crisp form but we may have only uncertain information of the data. Hence, investigations are needed to handle the SI problems where data is available in uncertain form. Identification methods with crisp (exact) data are known and traditional neural network methods have already been used by various researchers. But when the data are in uncertain form then traditional ANN may not be applied. Accordingly, new ANN models need to be developed which may solve the targeted uncertain SI problems. Hence present investigation targets to develop powerful methods of neural network based on interval and fuzzy theory for the analysis and simulation with respect to the uncertain system identification problems. In this thesis, these uncertain data are assumed as interval and fuzzy numbers. Accordingly, identification methodologies are developed for multistorey shear buildings by proposing new models of Interval Neural Network (INN) and Fuzzy Neural Network (FNN) models which can handle interval and fuzzified data respectively. It may however be noted that the developed methodology not only be important for the mentioned problems but those may very well be used in other application problems too. Few SI problems have been solved in the present thesis using INN and FNN model which are briefly described below. From initial design parameters (namely stiffness and mass in terms of interval and fuzzy) corresponding design frequencies may be obtained for a given structural problem viz. for a multistorey shear structure. The uncertain (interval/fuzzy) frequencies may then be used to estimate the present structural parameter values by the proposed INN and FNN. Next, the identification has been done using vibration response of the structure subject to ambient vibration with interval/fuzzy initial conditions. Forced vibration with horizontal displacement in interval/fuzzified form has also been used to investigate the identification problem. Moreover this study involves SI problems of structures (viz. shear buildings) with respect to earthquake data in order to know the health of a structure. It is well known that earthquake data are both positive and negative. The Interval Neural Network and Fuzzy Neural Network model may not handle the data with negative sign due to the complexity in interval and fuzzy computation. As regards, a novel transformation method have been developed to compute response of a structural system by training the model for Indian earthquakes at Chamoli and Uttarkashi using uncertain (interval/fuzzified) ground motion data. The simulation may give an idea about the safety of the structural system in case of future earthquakes. Further a single layer interval and fuzzy neural network based strategy has been proposed for simultaneous identification of the mass, stiffness and damping of uncertain multi-storey shear buildings using series/cluster of neural networks. It is known that training in MNN and also in INN and FNN are time consuming because these models depend upon the number of nodes in the hidden layer and convergence of the weights during training. As such, single layer Functional Link Neural Network (FLNN) with multi-input and multi-output model has also been proposed to solve the system identification problems for the first time. It is worth mentioning that, single input single output FLNN had been proposed by previous authors. In FLNN, the hidden layer is replaced by a functional expansion block for enhancement of the input patterns using orthogonal polynomials such as Chebyshev, Legendre and Hermite, etc. The computations become more efficient than the traditional or classical multi-layer neural network due to the absence of hidden layer. FLNN has also been used for structural response prediction of multistorey shear buildings subject to earthquake ground motion. It is seen that FLNN can very well predict the structural response of different floors of multi-storey shear building subject to earthquake data. Comparison of results among Multi layer Neural Network (MNN), Chebyshev Neural Network (ChNN), Legendre Neural Network (LeNN), Hermite Neural Network (HNN) and desired are considered and it is found that Functional Link Neural Network models are more effective and takes less computation time than MNN. In order to show the reliability, efficacy and powerfulness of INN, FNN and FLNN models variety of problems have been solved here. Finally FLNN is also extended to interval based FLNN which is again proposed for the first time to the best of our knowledge. This model is implemented to estimate the uncertain stiffness parameters of a multi-storey shear building. The parameters are identified here using uncertain response of the structure subject to ambient and forced vibration with interval initial condition and horizontal displacement also in interval form

Enabling smart city resilience: post-disaster response and structural health monitoring

The concept of Smart Cities has been introduced to categorize a vast area of activities to enhance the quality of life of citizens. A central feature of these activities is the pervasive use of Information and Communication Technologies (ICT), helping cities to make better use of limited resources. Indeed, the ASCE Vision for Civil Engineering in 2025 (ASCE 2007) portends a future in which engineers will rely on and leverage real-time access to a living database, sensors, diagnostic tools, and other advanced technologies to ensure that informed decisions are made. However, these advances in technology take place against a backdrop of the deterioration of infrastructure, in addition to natural and human-made disasters. Moreover, recent events constantly remind us of the tremendous devastation that natural and human-made disasters can wreak on society. As such, emergency response procedures and resilience are among the crucial dimensions of any Smart City plan. The U.S. Department of Homeland Security (DHS) has recently launched plans to invest $50 million to develop cutting-edge emergency response technologies for Smart Cities. Furthermore, after significant disasters have taken place, it is imperative that emergency facilities and evacuation routes, including bridges and highways, be assessed for safety. The objective of this research is to provide a new framework that uses commercial off-the-shelf (COTS) devices such as smartphones, digital cameras, and unmanned aerial vehicles to enhance the functionality of Smart Cities, especially with respect to emergency response and civil infrastructure monitoring/assessment. To achieve this objective, this research focuses on post-disaster victim localization and assessment, first responder tracking and event localization, and vision-based structural monitoring/assessment, including the use of unmanned aerial vehicles (UAVs). This research constitutes a significant step toward the realization of Smart City Resilience

Stress development of a supertall structure during construction : field monitoring and numerical analysis

Author name used in this manuscript: Yi-qing Ni2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Opportunities and Challenges in Health Monitoring of Constructed Systems by Modal Analysis

Dynamic testing of constructed systems was initiated in the 1960’s by civil engineers interested in earthquake hazards mitigation research. During the 1970’s, mechanical engineers interested in experimental structural dynamics developed the art of modal analysis. More recently in the 1990’s, engineers from different disciplines have embarked on an exploration of health monitoring as a research area. The senior writer started research on dynamic testing of buildings and bridges during the 1970’s, and in the 1980’s collaborated with colleagues in mechanical engineering who were leading modal analysis research to transform and adapt modal analysis tools for structural identification of constructed systems. In the 1990’s the writer and his associates participated in the applications of the health monitoring concept to constructed systems. In this paper, the writers are interested in sharing their experiences in dynamic testing of large constructed systems, namely, MIMO impact testing as well as output-only modal analysis, in conjunction with associated laboratory studies. The writers will try to contribute to answering some questions that have been discussed in the modal analysis and health monitoring community for more than a decade: (a) What is the reliability of results from dynamic testing of constructed systems, (b) Can these tests serve for health monitoring of constructed systems? (c) Are there any additional benefits that may be expected from dynamic testing of constructed systems? (d) Best practices, constraints and future developments needed for a reliable implementation of MIMO testing and output-only modal analysis of constructed systems for health monitoring and other reasons