Complex Civil Infrastructures Research Articles

Bayesian inference of dense structural response using vision-based measurements

As-built structures typically behave differently than their analytical models because of uncertainties in material properties, boundary conditions, loading scenarios, or other modeling assumptions. Therefore, to perform structural health monitoring effectively, analytical models need to be calibrated or updated to match the measured responses from as-built structures. Bayesian model updating provides a rigorous framework to integrate data and parameter uncertainty and has been applied successfully in numerous application settings employing traditional sensors that are often sparsely distributed. Computer vision has shown tremendous success for the measurement of static and dynamic displacements of structures. Vision-based measurements have the potential to be transformative for model updating of large-scale civil infrastructure, as measurements can be obtained for dense array of points within the camera frame using a single device. However, model updating using vision-based measurements to date has been limited to the use of a few points selected manually, leading to the investigation of simple structures or their global behaviors (e.g. first few modal responses); developing a systematic and highly automated procedure for estimating structural displacement and its uncertainty densely, registering the measured data to the finite element model, and then updating the model based on all the available data remains a challenge. This study proposes Bayesian inference using dense vision-based measurements to estimate the localized response of the entire structure. Finite element models are incorporated into the three-dimensional displacement measurement and registration process (model-informed approach), and then the Markov chain Monte Carlo method is applied to enable the Bayesian inference for large and complex civil infrastructure. The proposed method is applied to a laboratory-scale three-dimensional steel truss to perform inference of the overall system response based on the a posteriori knowledge. The results demonstrate the adequacy of performing Bayesian inference of structural response using vision-based measurements.

Damage detection in nonlinear civil structures using kernel principal component analysis

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

Modular Bayesian damage detection for complex civil infrastructure

We address the problem of damage identification in complex civil infrastructure with an integrative modular Bayesian framework. The proposed approach uses multiple response Gaussian processes to build an informative yet computationally affordable probabilistic model, which detects damage through inverse updating. Performance of structural components associated with parameters of the developed model was quantified with a damage metric. Particular emphasis is given to environmental and operational effects, parametric uncertainty and model discrepancy. Additional difficulties due to usage of costly physics-based models and noisy observations are also taken into account. The framework has been used to identify a reduction of a simulated cantilever beam elastic modulus, and anomalous features in main/stay cables and bearings of the Tamar bridge. In the latter case study, displacements, natural frequencies, temperature and traffic monitored throughout one year were used to form a reference baseline, which was compared against a current state, based on one month worth of data. Results suggest that the proposed approach can identify damage with a small error margin, even under the presence of model discrepancy. However, if parameters are sensitive to environmental/operational effects, as observed for the Tamar bridge stay cables, false alarms might occur. Validation with monitored data is also highlighted and supports the feasibility of the proposed approach.

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods.

Five years of the Journal of Civil Structural Health Monitoring

Let me begin by wishing the Journal of Civil Structural Health Monitoring (JCSHM) a very happy birthday. JCSHM is now 5 years old! The journal has come a long way since its humble beginnings in 2011. The first volume of JCSHM had two issues with a total of 124 pages, whereas volume number five in 2015 was comprised of 5 issues and 765 pages. We will readily surpass 1000 pages in 2016, and increase the number of issues in 2017. JCSHM is dedicated to publishing practice-oriented articles and specifically aims at providing technical information about the state-of-practice and technologies of importance for maintenance and safety of all types of civil infrastructure systems. This is a niche that the journal has consistently filled and no other journals have been able to publish articles specific to best practices in technologies impacting the discipline of Civil Structural Health Monitoring. We owe this success to the authors, reviewers, and the editorial board consisting of 17 members from 10 different countries that are internationally recognized as experts in the field of civil structural health monitoring. The board members strive to deliver a journal that includes high-quality, peerreviewed articles. JCSHM is the brainchild of ISHMII, which is an international society with global perspective to advance the understanding and the application of civil structural health monitoring. Membership covers a broad range of engineers and practitioners, members of the academic community, and infrastructure managers and students. The high standards set by ISHMII have been essential and will sed range of engineers and practitioners, members of the academic community, and infrastructure managers and students. The high standards set by ISHMII have been essential and will serve as a guiding light for the expansion of journal contents. JCSHM recognizes excellence, and awards the authors of the best paper from each volume with the Aftab Mufti Medal. The medal has been awarded four times since its inception in December 2011. The Aftab Mufti Medal honors the service, dedication, and lifetime achievements of Dr. Aftab Mufti, cofounder and the first president of ISHMII. To celebrate JCSHM’s 5 years of existence, we will be publishing state-of-the-art articles in several issues of volume six in 2016. Due to the interdisciplinary expertise and interests of the journal’s readers, we anticipate that the sixth volume will provide an invaluable resource for researchers and practitioners in civil structural monitoring. Six of the state-of-the-art articles are published in this issue of the journal, covering a wide spectrum of topics of interest to our readers. These topics include monitoring of complex civil infrastructure systems, wireless smart sensors, cable-stayed bridges, bridge weigh-in-motion systems, experiences in monitoring of heritage structures in Italy, and inverse problems in vehicle–bridge interaction dynamics. In addition to the state-of-the-art articles, the regular papers in this first issue cover topics such as piezoelectric sensors for detection of cracks in concrete & Farhad Ansari fansari@uic.edu

State-of-the-art in structural health monitoring of large and complex civil infrastructures

Researchers and administrative authorities have long recognized the significance of implementing long-term structural health monitoring (SHM) systems for civil infrastructures, especially large-span space structures or other complex infrastructures, in order to secure structural safety and issue warnings regarding the structural damage prior to costly repair or even collapse. This paper starts with a critical review of SHM objectives and standardization for the SHM in civil infrastructures. At the same time as an example, the authors present their experiences through the case study of SHM applications for large and complex civil infrastructures through a custom-made polytypic and synchronous data acquisition device that was utilized in the SHM of the large-span gymnasium in Dalian. The paper concludes with a discussion of state-of-the-art in the SHM for civil infrastructures, highlighting areas where further work is needed.

Assessment and Management of Concrete Bridges Supported by Monitoring Data-Based Finite-Element Modeling

The long-term assessment of large and complex civil infrastructures, such as prestressed concrete bridges, based on Finite Element (FE) modelling remains a challenging task. The construction process, the influence of erection methods, the characterization of concrete properties, geometric accuracy and environmental conditions are key factors involved in the development of robust FE models. Data collected using permanently installed monitoring systems is the most reliable strategy to improve such assessments. Indeed, the availability of monitoring data is increasingly being used in the validation of design assumptions, updating of FE models and safety factors. In this work, the long-term behaviour of a long segmental bridge built in Portugal − Leziria Bridge − is evaluated using FE model-updating. The combination of several factors, including: (i) the bridge’s scale, (ii) the monitoring database, (iii) the comprehensive scanning of important characteristics of the bridge and (iv) the FE modelling approach, makes this case study unique. Although the sensor trends are satisfactorily predicted, extrapolation of shrinkage and creep models, the influence of interior and exterior environments and thickness variations of the structural elements are identified as areas for further research.

구조물 건전성 모니터링을 위한 증강현실 기반 센서 위치인식 및 데이터시각화 기술

최근 초고층 및 초장대와 같은 대형 구조물들이 시공되고 있으며, 이에 대한 구조물 건전성 모니터링 기술들이 연구되고 있다. 하지만 기존의 기술들은 계측 센서의 관리와 센서로부터 계측된 데이터에 효율적으로 access하지 못하고 있다. 본 논문에서는 구조물 건전성 모니터링을 위한 증강현실 기반 센서 위치인식 및 데이터 시각화 기술을 소개한다. 모바일 디바이스에 내장된 GPS를 통하여 센서와 사용자 간의 거리를 파악하게 된다. 뿐만 아니라, 센서로부터 계측된 데이터는 위치정보시스템 서버에 저장되며, RSS방식을 통해 전송되어 사용자가 모바일 디바이스를 통해 쉽게 계측 데이터를 가시화 할 수 있게 된다. 이 기술을 이용하여 사용자는 센서의 위치인식을 통해 계측센서를 관리하고, 계측 데이터를 시각화하여 시간과 공간에 제약 없이 구조물의 건전성을 모니터링 할 수 있게 된다. In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

Augmented Reality-Based Embedded Sensor Location Recognition and Data Visualization Technique for Concrete Structural Health Monitoring

In recent years, numerous mega-sized and complex civil infrastructures are being constructed all over the world. Therefore, more precise construction and maintenance technologies are required for these complicated construction projects. So a variety of sensors-based structural health monitoring (SHM) techniques have been studied, but these techniques could not manage the sensors efficiently access the database obtained from the sensors. Recently, Quick response (QR) code and AR-based data access technologies have been developed. In this paper, an AR-based concrete curing strength monitoring technique for sensor management and efficient access of the measured data is introduced. It is confirmed that the AR-based concrete curing strength monitoring technique is useful for construction process. In addition, it is concluded that both efficient sensor location recognition and data visualization at anytime, anywhere, and any smart PC devices are promising.

Sensing Methodologies and Sensor Networks for Health Monitoring of Civil Infrastructures

Large and complex civil infrastructures are especially susceptible to random vibrations, whether they are from high ground accelerations, strong wind forces, or abnormal loads such as explosions. Thus, it is imperative that the robust and continuous health monitoring system needs to be designed and implemented so as to meet the demanding goals of increasing structural safety and reliability, while reducing its operating and maintenance costs. The efficiency of a structural health monitoring system (SHMS) relies on the sensitivity of the acquired data to structural changes that may be obtained by an extended sensor network on structures as shown in “Methodology developments in sensor placement for health monitoring of civil infrastructures” and [1]. Recent advances in sensing technologies have enabled the use of large numbers of sensors for health monitoring of civil infrastructures. The sensors utilized in an SHMS are required to monitor not only the structural status including the stress, displacement, and acceleration but also those influential environmental parameters, such as the wind speed, temperature, and quality of its foundation. Since a large sensor network needs to be involved in an SHMS, how to deploy it in a complicated, spatially extended structure so that the data acquired from those locations will result in the best identification of structural characteristics will be a challenging task. On the other hand, a high cost of data acquisition systems (sensors and their supporting instruments) and accessibility limitation constrain in many cases the wide distribution of a large sensor networks on a structure. Especially, many civil structures have to be tested under operational conditions, in which the sensors are not easily amenable to be removed or changed [2, 3]. It is, therefore, of increasing interests to seek rational placement and design a method of sensor networks that contain as much information as possible about the health state of a structure. This special issue aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in the integration of various aspects of SHMS applications within a broad range of disciplines. Some research topics towards this target include intelligent and efficient algorithms in the design of sensor networks; evaluation criteria for different sensor networks placement methods; inherent relationship between different sensor networks placement methods; uncertainty, sensitivity, reliability, and redundancy in sensor networks; application of large scale sensors for civil infrastructures and others related. A total of 26 peer-reviewed papers have been presented in this special issue. These include identifying the optimal number and location of sensors, as well as the robustness, which aims at maintaining the stability of the sensor network when some sensors malfunction, and so forth. Without a doubt, the papers reflect the state-of-the-art researches and developments of this subject. The Guest Editor would like to express his sincere appreciation to all the authors who have contributed their work to this special issue.

콘크리트 생애주기 품질관리를 위한 QR 코드 기반 강도 라벨링 기술

국내외적으로 수주량이 증가하고 있는 대형 구조물의 건설 시 보다 정밀한 시공 및 유지관리 기술이 요구된다. 그 중 콘크리트의 강도는 대표적인 품질관리 변수 중 하나로, 정확한 강도 값의 측정 및 이력관리는 건설 프로세스에서의 공기단축을 통한 비용 절감 및 효율적인 시공관리를 위해 매우 중요한 요구 사항이다. 이에 이 논문에서는 유비쿼터스 시대에 적합한 건설시공기술로의 발전을 위해 최근 개발된 임베디드 자율 감지형 콘크리트 강도 모니터링 기술을 데이터베이스화하고 이를 QR(quick response)코드와 연동시키는 콘크리트 강도 라벨링 기술을 소개한다. 이를 통하여 콘크리트 구조물의 강도 이력 DB를 언제 어디서나 실시간으로 확인하고 이를 바탕으로 보다 정밀하고 경제적인 시공 및 유지관리할 수 있는 차세대 콘크리트 생애주기 품질관리 시스템으로의 실현 가능성에 대해 고찰해본다.

False targets vs. redundancy in homogeneous parallel systems

System defense against natural threats and disasters that have a stochastic nature includes providing redundancy and protecting system elements. The defense against strategic intentional attacks can also include deploying false targets aimed at misleading the attacker. Distribution of the available resources among different defensive means is an important problem that arises in organizing the defense of complex civil infrastructures, industrial systems or military objects. The article considers defense resource allocation in a system exposed to external intentional attack. The expected damage caused by the attack is evaluated as system unsupplied demand. The defender distributes its limited resource between deploying redundant genuine elements and false elements, both of which are targets of attack. The attacker attacks a subset of the elements and distributes its limited resource evenly among the attacked elements. Two cases are considered: in the first one the number of attacked elements and the vulnerability of each genuine element are fixed and the defense resource distribution is determined as a solution of an optimization problem; in the second one the number of attacked elements is the attacker's free choice variable and the element's vulnerability depends on a contest determined by the defender's and attacker's resources allocated to each element. The defender's optimal resource distribution strategy is determined as a solution of a two-period minmax game. It is shown that the optimal number of genuine elements decreases monotonically with the growth of the element cost and vulnerability, whereas the optimal number of false elements demonstrates non-monotonic behavior. The contest intensity is an important factor influencing the optimal defense resource distribution. It cannot be ignored when the defense strategy is determined, and it thus also impacts the attack strategy.

Adaptive importance sampling for risk analysis of complex infrastructure systems

Complex civil infrastructure systems are typically exposed to random loadings and have a large number of possible failure modes, which often exhibit spatially and temporally variable consequences. Monte Carlo (level III) reliability methods are attractive because of their flexibility and robustness, yet computational expense may be prohibitive, in which case variance reduction methods are required. In the importance sampling methodology presented here, the joint probability distribution of the loading variables is sampled according to the contribution that a given region in the joint space makes to risk, rather than according to probability of failure, which is the conventional importance sampling criterion in structural reliability analysis. Results from simulations are used to intermittently update the importance sampling density function based on the evaluations of the (initially unknown) risk function. The methodology is demonstrated on a propped cantilever beam system and then on a real coastal dike infrastructure system in the UK. The case study demonstrates that risk can be a complex function of loadings, the resistance and interactions of system components and the spatially variable damage associated with different modes of system failure. The methodology is applicable in general to Monte Carlo risk analysis of systems, but it is likely to be most beneficial where consequences of failure are a nonlinear function of load and where system simulation requires significant computational resources.