Civil Infrastructure Monitoring Research Articles

Strain-Detecting properties of hybrid PE and steel fibers reinforced cement composite (Hy-FRCC) with Multi-Walled carbon nanotube (MWCNT) under repeated compression

This experimental research investigates the feasibility of using hybrid polyethylene (PE) and steel fibers reinforced cement composite (Hy-FRCC) with multi-walled carbon nanotube (MWCNT) as strain sensors. Smart construction material presented in the study is a new type of multifunctional cement composite with both structural material and self-sensing functionalities. The effect of MWCNT dosage (1 and 2% by weight of cement) and the magnitude of repeated compressive stress on the strain-detecting function and elastic modulus was studied. The history of applied compressive loading consists of five successive levels (30, 50, 60, 70 and 80% of compressive strength) of stress amplitude to evaluate the repeated strain-sensing ability of Hy-FRCC. Test results revealed that the incorporation of MWCNT within 2% has little effect on the compressive strength of Hy-FRCC at curing ages of 28-day while under repeated compression, MWCNT is effective to decrease the reduction of the elastic modulus in the Hy-FRCC. Piezoresistivity experiments indicated that the change in resistivity of Hy-FRCC-2 (with 2% MWCNT) specimen can simulate the changes in the measured strain during repeated compressive loading at increasing stress amplitudes up to 80% of compressive strength. It is found that the Hy-FRCC-2 mixture proposed in the present study is likely to be used as strain-sensors for structural health monitoring (SHM) of civil infrastructures.

Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders

Damage detection is one of the most important tasks for structural health monitoring of civil infrastructure. Before a damage detection algorithm can be applied, the integrity of the data must be ensured; otherwise results may be misleading or incorrect. Indeed, sensor system malfunction, which results in anomalous data (often called faulty data), is a serious problem, as the sensors usually must operate in extremely harsh environments. Identifying and eliminating anomalies in the data is crucial to ensuring that reliable monitoring results can be achieved. Because of the vast amounts of data typically collected by a structural health monitoring system, manual removal of the anomalous data is prohibitive. Machine learning methods have the potential to automate the process of data anomaly detection. Although supervised methods have been proven to be effective for detecting data anomalies, two unresolved challenges reduce the accuracy of anomaly detection: (1) the class imbalance and (2) incompleteness of anomalous patterns of training dataset. Unsupervised methods have the potential to address these challenges, but improvements are required to deal with vast amounts of monitoring data. In this article, the generative adversarial networks are combined with a widely applied unsupervised method, that is, autoencoders, to improve the performance of existing unsupervised learning methods. In addition, the time-series data are transformed to Gramian Angular Field images so that advanced computer vision methods can be included in the network. Two structural health monitoring datasets from a full-scale bridge, including examples of anomalous data caused by sensor system malfunctions, are utilized to validate the proposed methodology. Results show that the proposed methodology can successfully identify data anomalies with good accuracy and robustness, hence can overcome one of the key difficulties in achieving automated structural health monitoring.

Intermittent Contextual Learning for Keyfilter-Aware UAV Object Tracking Using Deep Convolutional Feature

Visual tracking, one of the most favorable multimedia applications, has been widely used in unmanned aerial vehicle (UAV) for civil infrastructure monitoring, aerial cinematography, autonomous navigation, etc. Most existing trackers utilize deep convolutional feature to enhance tracking robustness in scenarios of various appearance variation. However, they commonly neglect speed which is crucial for UAV with restricted calculation resources. In this work, a novel correlation filter-based keyfilter-aware tracker with a new intermittent context learning strategy is proposed to efficiently and effectively alleviate the problems of background clutter, deficient description, occlusion, illumination change, etc. Specifically, context information is utilized to empower the filter higher discriminating ability through response repression of the omnidirectional context patches. Furthermore, keyfilter is produced from the periodically selected keyframe. The latest produced keyfilter is used to restrain the current filter's corrupted changes. Most importantly, context learning of correlation filter is implemented intermittently to fully increase the tracking efficiency. This intermittent learning strategy can ensure every filter maintain context awareness owing to the restriction of keyfilter, periodically enhancing the context awareness. Substantial experiments on three challenging UAV benchmarks totally with 213 image sequences have shown that our tracker surpasses the state-of-the-art results, and exhibits a remarkable generality in short-term and long-term UAV tracking tasks as well as a variety of challenging attributes.

Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.

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.

Structural modal identification and health monitoring of building structures using self-sensing cementitious composites

Recently self-sensing cementitious composite has demonstrated its strong potentiality for structural health monitoring of civil infrastructures because of its low-cost, long-term stability and compatibility with concrete structures. In this paper, we propose novel hybrid nanocarbon materials engineered cement-based sensors (HNCSs) with high-sensitivity, which are fabricated with self-sensing cementitious composites containing electrostatic self-assembled CNT/NCB composite fillers. The mechanical property and sensing performance of the HNCSs are pre-characterized under static and dynamic compressive loadings. The HNCSs are then integrated into a five-story building model via custom-made clamps to verify the feasibility for dynamic response measurements. Results show that the developed sensors have satisfactory mechanical property and excellent pressure-sensitive reproducibility and stability. With clamps holding on the building model, the HNCSs perform satisfactorily under sinusoidal excitations in the frequency range from 2 to 40 Hz. In addition, the modal frequencies and their changes of the building model caused by ‘damage’ simulated through adding additional masses identified by the HNCSs are favorably consistent with the counterparts acquired by accelerometers and strain gauges, indicating that the developed HNCSs have great potential for structural modal identification and damage detection applications.

3-D Imaging of Materials at 0.1 THz for Inner- Defect Detection Using a Frequency-Modulated Continuous-Wave Radar

This article reports a novel 3-D imaging approach for remote, nondestructive material inspection at depth. The approach is based on a frequency-modulated continuous-wave (FMCW) radar system operating in the 75-110-GHz frequency band. Time-domain spectroscopy (TDS) measurements demonstrate that such a frequency band has manage-able attenuation levels in centimeter-long materials, thereby offering an advantageous resolution-versus-measurement-depth tradeoff compared with higher frequencies. The proposed setup includes a long delay on the reference signal in order to cancel redundant signals that would otherwise degrade the measurement. Furthermore, specific signal-processing techniques used to improve the performance of the system, which requires stringent component performances in terms of radio-frequency nonlinearity and return loss, are presented. Radio-frequency nonlinearity correction is based on a reference measurement, and the impact of deleterious reflections is reduced by averaging measurements conducted on a target at different longitudinal positions. As a result, an improvement of the signal-to-noise (S/N) ratio up to 20 dB is demonstrated. Tomography experiments were conducted on building materials showing 3-D imaging with theoretically limited longitudinal and transverse resolutions of 5 and 20 mm, respectively, thus opening the door to civil infrastructure monitoring applications.

Dynamic sensing properties of a multifunctional cement composite with carbon black for traffic monitoring

The growing concern in monitoring civil infrastructures, combined with recent technological advances in sensing and information technology led to a concept known as ‘smart infrastructures’. Recently, nanotechnology has been contributing with the development of innovative and multifunctional building materials. For example, cementitious composites with specific conductive composition allow the detection of mechanical stresses and may be used not only to build infrastructures but to monitor them as well. In this paper, a stress-sensitive cement composite integrating a conductive carbon black filler (CB) is investigated for traffic monitoring purposes, relying on piezoresistivity. The sensitivity of the composite was evaluated in specific specimens by performing traffic-like loading experiments, at temperatures between 15 °C and 45 °C. Experiments showed linear and reversible piezoresistive responses with average gauge factors registered between 40 and 50 and demonstrated that such composites may act as stress-sensitive materials for traffic monitoring, such as traffic flow, vehicular speed and weigh-in-motion measurements.

A Machine Learning Approach for Structural Health Monitoring Using Noisy Data Sets

Continuous structural health monitoring of civil infrastructure can be achieved by deploying an Internet of Things network of distributed acceleration sensors in buildings to capture floor movement. Postdisaster damage levels can be computed based on the peak relative floor displacement as specified in government standards. This article uses machine learning approaches to identify the status of buildings postevent based on accelerometer traces. Prior work in the field assumed the use of high-quality accelerometers for displacement estimation. In this article, we focus on using lower quality and cheaper accelerometers, while accounting for noise effects by incorporating noisy data sets in machine learning approaches for classification. A labeled acceleration data set of buildings response to earthquakes was created, where each sample is labeled with its corresponding damage severity. Sensor noise is included in the data set to model nonideal sensors. Classification performance of machine learning algorithms, such as support vector machine, K-nearest neighbor, and convolutional neural network, is presented. Techniques for addressing noise levels are proposed, and the results are compared with regular noise cancellation techniques that adopt high-pass filtering. Note to Practitioners —This article presents a methodology for automatic estimation of buildings status in the aftermath of a natural disaster, such as an earthquake. It focuses on using low-cost inertial sensors, such as accelerometers, to sense buildings’ vibrations and then applying machine learning algorithms to detect damage. Utilizing the convolutional network approach, the proposed methods detect the building damage state with high accuracy. Since this article focuses on using cheap sensors, the cost of deploying a sensor network to monitor buildings is reduced significantly. Deploying this network enables rescue and reconnaissance teams to have a clear view of the most vulnerable structures.

Determining the number of measurements for compressive sensing of traffic-induced vibration data

Compressive Sensing (CS) is an emerging signal sampling technique, which can be useful in the long-term monitoring of civil infrastructures by reducing the storage space and transmission bandwidth drastically. In this paper, a fully deterministic approach is adopted to enhance the previously proposed CS-based methods. First, the sparsifying dictionary is trained using a vibration data set. Then, a deterministic projection matrix is computed based on the trained dictionary. Second, a new index is defined to determine the number of measurements in advance, without any trial and error in the reconstruction stage. This index which is coined as NPI (Normalized Power Index) is derived using the singular value decomposition of the trained dictionary. The capability of the proposed method is investigated using vibration signals of the Tianjin Yonghe Bridge with traffic excitation. Both accuracy and computational time of the deterministic CS was compared to different data compression algorithms.

Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission

The consideration of wireless acceleration sensors is highly promising for cost-effective output-only system identification in the context of operational modal analysis (OMA) of large-scale civil structures as they alleviate the need for wiring. However, practical monitoring implementations for OMA using wireless units suffer a number of drawbacks related to wireless transmission of densely sampled acceleration time-series including the energy self-sustainability of the sensing nodes. In this work, two recently proposed approaches for output-only modal identification addressing the above issues through balancing monitoring accuracy with data transmission costs are comparatively studied and numerically assessed using field recorded acceleration datasets from two different structures: (i) an operating on-shore wind turbine, (ii) an open to traffic highway bridge. One approach utilizes non-uniform-in-time deterministic multi-coset sampling at sub-Nyquist rates to capture structural response acceleration time-series under ambient excitation assuming stationary signal conditions. In this approach, a power spectrum blind sampling technique is used to estimate the response acceleration power spectral density matrix from the low-rate sampled measurements and is coupled with the Frequency Domain Decomposition method of OMA. The other is a spectro-temporal compressive sensing approach which recovers response acceleration signals through time-series reconstruction in the time domain from sub-Nyquist non-uniform-in-time randomly sampled measurements. Prior knowledge of signal structure in the spectral domain is exploited through smart on-sensor operations and sensor/server communication. The benefits and limitations of the considered approaches are discussed and demonstrated by processing the field recorded datasets for different levels of signal compression and by estimating battery lifetime gains at a single sensor achieved by reduced data transmission. It is concluded that the two approaches are readily applicable in OMA of large-scale structures and can be used complementarily depending on the requirements of any particular acceleration monitoring campaign: time-series extraction for further interrogation vs. solely modal properties estimation.

Evaluation of UAV–CRP Data for Monitoring Transportation Infrastructure Constructed over Expansive Soils

Application of unmanned aerial vehicles (UAVs) for civil infrastructure monitoring has gained impetus owing to the advancement of aerial platforms paralleled with the development of sophisticated sensors. Photogrammetry is the science of measuring distances from two or more images, and close-range photogrammetry (CRP) is a part of photogrammetry that involves calculating measurements of an object within a maximum distance of 305 m away from the inspecting sensors. Geotechnical problems including differential heaving and related cracking of expansive soils cause extensive damage to pavement infrastructure. Feasibility of using UAV–CRP technology in health monitoring of pavement infrastructure constructed over problematic soils has been comprehensively studied, and this paper presents an overview of these results. Pavement performance data including longitudinal and transverse slopes, as well as distress conditions such as pavement surface cracking, pothole formation, and rutting or excessive deformation, are monitored via UAV–CRP technology, and these data sets are comprehensively analysed. UAV–CRP-interpreted performance indicators showed a very good agreement with those obtained from traditional methods surveys and profiler studies. With the further research, the UAV–CRP technology will play an important engineering role in safe, inexpensive and comprehensive health monitoring of infrastructure built over problematic soil conditions.

Fragility analysis using vibration energy harvesters

Fragility curves are widely used as indicators of vulnerability of infrastructure to structural/performance demands posed on it by the environment. Fragility of a structure may change due to variety of factors during its lifetime. This renders the applicability of fragility for risk assessment to be reliant on periodically updated estimation. Derivation of fragility requires an estimate of the capacity of the structure and the demand due to external factors. Vibration data can be used as a medium to estimate both capacity and demand on the system. Energy harvesters being self-sufficient vibration sensors are proposed as devices capable of estimating fragility curves in lieu of other inertial sensors. The concept is illustrated here using simplified models. The reduction in system complexity due to the self-powered nature of the energy harvester along with the proposed ability to compute probability of failure make energy harvesters attractive options for monitoring civil infrastructure and thereby minimizes risk at various stages in its lifetime.

Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

AbstractHealth monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.

Durability and weatherability of a styrene-ethylene-butylene-styrene (SEBS) block copolymer-based sensing skin for civil infrastructure applications

Structural health monitoring of civil infrastructure requires low-cost, scalable, long-term, and robust sensing technologies due to the size and complexity of the geometries under consideration. This paper investigates the durability and weatherability of a large area sensing skin engineered for civil infrastructure applications. This sensing skin is based on a soft elastomeric capacitor made of three thin layers based on an SEBS block co-polymer matrix. The inner layer is filled with titania and acts as the dielectric, while the external layers are doped with carbon black and work as the conductive plates. In this work, a variety of specimens, including the dielectric layer without the conductive plates, were fabricated and tested within an accelerated weathering chamber by simulating thermal, humidity, and UV radiation cycles. Beyond the accelerated weathering tests, a sensor deployed on a bridge in Iowa for six and a half years was removed from the field and analyzed in the laboratory. A variety of other tests were performed in order to characterize the specimens’ mechanical, thermal, optical, and electrical performance. Additionally, strain sensitivity analyses were performed on specimens of interest. Results showed that titania inclusions improved the sensor dielectric's durability against weathering, while the carbon black doped conductive layers provided the skin sensor with a high level of durability and weatherability protection. The results in this work contribute to a better understanding of the degradation of SEBS-based matrices as well as the behavior of these skin sensors when deployed for the monitoring of civil infrastructure.

Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

Abstract Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.

An Automatic Modal Identification Procedure for the Permanent Dynamic Monitoring of the Sanctuary of Vicoforte

ABSTRACTThe interest in automatic modal parameter extraction techniques has increased significantly in recent years because of the rising demand for Continuous Structural Health Monitoring (CSHM) of civil structures and infrastructures. The wider use of CSHM is related to its capability for early damage detection and therefore to its usefulness for planning maintenance and strengthening interventions. This article presents the main steps followed in creating a robust routine to run on an online basis the first-ever Continuous Vibration-Based Structural Health Monitoring (CVB-SHM) of a large masonry oval dome, i.e., the dome of the Regina Montis Regalis Basilica, also known as Sanctuary of Vicoforte. The procedure uses an output-only stochastic subspace identification method complemented by an automatic analysis applied to identification results, which are typically provided in terms of stabilization diagrams. In this study, the fully automatic procedure also includes a hierarchical clustering algorithm to separate authentic from spurious modes.

An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring

Modern smartphones are equipped with various sensors along with on-board storage, computing and communication capabilities. Owing to these features, they can become an intelligent, scalable, autonomous and potentially cost-free component of the next generation civil infrastructure monitoring systems in future smart cities. Over the past few years, there has been a growing interest in the deployment of smartphone-based monitoring technologies within the civil engineering arena. Overall, the smartphone sensing paradigm is still in its infancy, with great promise for researchers to rapidly expand its many potential applications. This paper presents a comprehensive literature review of smartphone-centric research for the monitoring of civil infrastructure systems. The historical deployment of smartphones in major areas of civil engineering has been explored. An emphasis is placed on sensing capabilities of smartphones and their crowdsourcing power for monitoring several distinct civil infrastructure systems. Furthermore, a case study is presented to provide our most recent efforts in deployment of smartphones for evaluation of highway pavements and challenges ahead. Finally, limitations, challenges and future directions for widespread application of smartphone-driven monitoring systems are discussed. The survey implies that much research is still required to explore the power of crowdsourced smartphone-based measurements, and to branch out into new application domains.

A principled multiresolution approach for signal decomposition

One of the common issues with the Structural Health Monitoring (SHM) of civil infrastructure, is that signals measured from structures are nonstationary. The nonstationarity is the result of uncontrolled variations in the environmental and operational variations (EOVs) in the structure, which result from the fact that monitoring can only be carried out in situ. Unfortunately, the EOVs prove to be a problem for damage detection, as they can mask the onset of damage, itself a nonstationary process. The aim of the current paper is to demonstrate a principled, but simple, decomposition method, which can separate signals into stationary and nonstationary components; the stationary component can then be used as a feature for SHM. The method is based on a wavelet/multiresolution analysis of the signal of interest, followed by a sequence of wavelet level-by-level tests for stationary using the ADF (Augmented Dickey-Fuller) test statistic. The decomposition is illustrated using data from the famous SHM campaign on the Z24 Bridge. The paper should be of interest, not just to engineers, but to econometricians, or anyone concerned with nonstationary signal processing.