Civil Infrastructure Monitoring Research Articles

Smart Geosynthetics and Prospects for Civil Infrastructure Monitoring: A Comprehensive and Critical Review

Civil infrastructure monitoring with the aim of early damage detection and acquiring the data required for urban management not only prevents sudden infrastructure collapse and increases service life and sustainability but also facilitates the management of smart cities including smart transportation sectors. In this context, smart geosynthetics can act as vital arteries for extracting and transmitting information about the states of the strain, stress, damage, deformation, and temperature of the systems into which they are incorporated in addition to their traditional infrastructural roles. This paper reviews the wide range of technologies, manufacturing techniques and processes, materials, and methods that have been used to date to develop smart geosynthetics to provide rational arguments on the current trends and utilise the operational trends as a guide for predicting what can be focused on in future researches. The various multifunctional geosynthetic applications and future challenges, as well as operational solutions, are also discussed and propounded to pave the way for developing applicable smart geosynthetics. This critical review will provide insight into the development of new smart geosynthetics with the contribution to civil engineering and construction industries.

Investigation of the compressive self-sensing response of filler-free metakaolin geopolymer binders and coatings

Self-sensing cementitious materials are an effective method of monitoring existing infrastructure due to their high durability and high sensing performance. Geopolymers are ideal candidates for such applications due to their enhanced ionic mobility, high mechanical properties and compatibility with ordinary Portland cement structures. In this paper, self–sensing filler-free metakaolin geopolymer binders and coatings are explored under repeated compression. Polypropylene fibers were added to the mix design to tackle shrinkage issues making these materials more suitable for site and field applications. The sensing performance of metakaolin geopolymers is examined under various loading patterns and their strain sensing performance is characterized. The geopolymer sensors displayed high repeatability and stability under different loading regimes over multiple cycles. The gauge factor for non-fiber and fiber geopolymer binders ranged from 18.3 to 38.3. Similarly, the gauge factor for geopolymer coatings ranged from 20.7 to 43.2. Based on the findings of this study, these materials have displayed the potential as a viable means of monitoring civil infrastructure under repeated compression.

Multimode optical fiber strain monitoring for smart infrastructures

The in-service monitoring of civil infrastructures is an important task required to achieve their smart operation. This task requires the installation of sensors to continuously check and control the structures’ status in order to fulfil the demanded operating conditions and avoid the occurrence of malfunctions. This work presents an investigation of the use of multimode optical fiber sensors in detecting strain and vibration of infrastructures. The multimode fiber sensor is composed of a single mode – multimode – single mode concatenated fiber structure. To explore its practical application for strain monitoring, the sensor is used to measure the strain on a pressurized pipe and its performance compared with the well-developed fiber Bragg grating sensor. The detuning of spectral features of the multimode sensor transmission with increasing pipe pressure is found to have a good linearity (R2 ∼ 0.95) and is more sensitive to strain compared to that of the fiber Bragg grating.

A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ N−1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness.

Investigation of electrically isolated capacitive sensing skins on concrete to reduce structure/sensor capacitive coupling

Damage to bridges can result in partial or complete structural failures, with fatal consequences. Cracks develop in concrete infrastructure from fatigue loading, vibrations, corrosion, or unforeseen structural displacement. Effective long-term monitoring of civil infrastructure can reduce the risk of structural failures and potentially reduce the cost and frequency of inspections. However, deploying structural health monitoring technologies for crack detection on bridges is expensive, especially long-term, due to the density of sensors required to detect, localize, and quantify cracks. Previous research on soft elastomeric capacitors (SECs) has shown their viability for low-cost monitoring of cracks in transportation infrastructure. However, when deployed on concrete for strain monitoring, a structure/sensor capacitive coupling exists that may cause a significant amplification in the signal collected from the SEC sensor. This work provides a detailed experimental study of electrically isolating capacitive sensing skins for concrete structures to reduce the structure/sensor capacitive coupling of an electrically grounded sensor. The study illustrates that the use of rubber isolators effectively decreases the capacitive coupling between concrete, which inherently has capacitive properties, and sensors such as the SEC that utilize capacitance measurements. In addition, the required thickness of isolation for accurate strain monitoring using the SEC with geometry described in the paper is investigated and better strain correlation is observed between the rubber of isolation thickness 0.30 mm and 0.64 mm with rubber of isolation of approximately 0.40 mm having the best response. Tests were conducted on small-scale concrete beams, and results were validated on full-scale reinforced concrete bridge decks recently taken out of service. This study demonstrates that with proper isolation material, the SEC can accurately transduce strain from concrete within a 10 error for strain levels beyond 25 .

Pixel-level classification for bridge deck rebar detection and localization using multi-stage deep encoder-decoder network

Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure have been active areas of research for the past few decades. The traditional inspection methods for civil infrastructure, mostly relying on the visual inspection are time-consuming, labor-intensive, error-prone, and often provide subjective results. In the wake of rising costs for infrastructural maintenance, the time factor, safety issues, and the error-prone nature of human inspection methods, there is an increased need for the development of automated methods for bridge inspection and maintenance. The purpose of this research is to provide a novel Deep Learning-based approach for rebar detection and localization within bridge decks. The proposed system is trained using Ground Penetrating Radar (GPR) data from 8 real bridges in the United States. The results have been discussed in terms of qualitative and quantitative aspects with considerable potential and various issues that need to be explored in future works. Due to the similarity in the type of parabolic signatures present in other GPR-related applications, this technique can be generalizable to other applications. The proposed approach for rebar detection and localization has considerable implications for the civil experts in general and GPR community in particular.

High-Sensitivity Wireless Sensing Using Amorphous Magnetic Microwires

Glass-coated amorphous ferromagnetic microwires subjected to variations in mass loading and parallel wire arrays with 0.5–6-cm interwire spacing were found to deliver exceptional magnetomechanical and wireless giant magnetoimpedance (GMI) responses, in the kilohertz and microwave range, respectively. The microwires allow wireless quantification of microgram mass differences: the magnetomechanical resonance frequency measured in zero applied field demonstrates an approximately linear decrease of 3 Hz/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{g}$ </tex-math></inline-formula> , and a sensitivity response that is ten times greater than that reported for commercial METGLAS-type amorphous magnetic ribbons of comparable length. Microwave giant magnetoimpedance data collected from planar arrays of parallel microwires show either constructive or destructive interference when compared to data obtained from a single microwire. The exceptional responsiveness of the glass-coated amorphous ferromagnetic microwires to mass loading and to geometric arrangement, along with their small diameter and ease of fabrication, highlights their promise for a wide variety of sensor applications, including biosensing, civil infrastructure monitoring, and high-throughput remote detection schemes.

A DBSCAN-based automated operational modal analysis algorithm for bridge monitoring

Advanced data analysis techniques are of paramount importance for the Structural Health Monitoring (SHM) of civil buildings and infrastructures. In particular, Automated Operational Modal Analysis (AOMA) algorithms are necessary for the output-only monitoring of such massive and large structures. The unsupervised estimation of their modal parameters from ambient vibrations enables assessing their integrity efficiently and continuously. This is particularly important for reinforced concrete (RC) bridges, which need constant maintenance. In this context, the classic cluster-based, multi-stage approach is effective in cleaning the stabilisation diagram and discerning stable and unstable modes. However, due to the shortcomings of binary classification with (k = 2)-means clustering, the labelling between ‘possibly physical’ and ‘certainly spurious’ modes may not be completely reliable. The procedure described here applies Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to bypass this limitation. This allows, among other advantages, to automatically detect and remove outliers, differently from the traditional techniques. The algorithm is fully automated, including the data-driven setting of DBSCAN parameters. Its viability is tested here on a real, full-scale case study, the Z24 road bridge dataset.

A Machine Learning Enabled Hall-Effect IoT-System for Monitoring Building Vibrations

Vibration monitoring of civil infrastructures is a fundamental task to assess their structural health, which can be nowadays carried on at reduced costs thanks to new sensing devices and embedded hardware platforms. In this work, we present a system for monitoring vibrations in buildings based on a novel, cheap, Hall-effect vibration sensor that is interfaced with a commercially available embedded hardware platform, in order to support communication toward cloud based services by means of IoT communication protocols. Two deep learning neural networks have been implemented and tested to demonstrate the capability of performing nontrivial prediction tasks directly on board of the embedded platform, an important feature to conceive dynamical policies for deciding whether to perform a recognition task on the final (resource constrained) device, or delegate it to the cloud according to specific energy, latency, accuracy requirements. Experimental evaluation on two use cases, namely the detection of a seismic event and the count of steps made by people transiting in a public building highlight the potential of the adopted solution; for instance, recognition of walking-induced vibrations can be achieved with an accuracy of 96% in real-time within time windows of 500ms. Overall, the results of the empirical investigation show the flexibility of the proposed solution as a promising alternative for the design of vibration monitoring systems in built environments.

Temperature independent FBG based displacement sensor for crack detection in civil structures

Structural health monitoring (SHM) of civil, industrial, and monumental infrastructures is increasing day by day to provide necessary vitals and avoid the hazardous incident. Crack detection is one of the primary health monitoring techniques for civil structures. A simple and temperature-independent optical displacement sensor using Fiber Bragg Gratings (FBG) for crack monitoring is proposed. The proposed sensing system utilizes a wavelength-to-time delay mapping-based interrogation technique to observe the strain generated on FBG. Initially, the sensor module is analyzed on FEM based simulation platform and later experimental analysis is carried out for the different displacement values which signify the width of the crack. A crack in the structure is detected in terms of time delay with respect to reference pulse. The proposed sensing system provides resolution and sensitivity of 0.18 mm and 8.95 ps/mm respectively. The proposed technique can provide stable temperature-independent sensing data that would minimize the probability of false crack detection in the structure and provide a much more reliable sensing solution.

Ring-core few-mode fiber and DPP-BOTDA-based distributed large-curvature sensing eligible for shape reconstruction.

This study proposes a distributed large-curvature sensor based on ring-core few-mode fiber (RC-FMF) and differential pulse-pair Brillouin optical time-domain analysis (DPP-BOTDA). The RC-FMF is adhered to a thin steel substrate and an asymmetric hump shape is reconstructed using the Frenet-Serret algorithm. The proposed curvature sensor demonstrates a larger curvature-sensing range, excellent tolerance to bending-induced optical loss, and increased Brillouin gain coefficient. The proposed sensor also demonstrates longer sensing distance and continuous absolute measurement compared to other sensors. The proposed model can be applied to the end tracking of soft robotics and structural health monitoring of civil infrastructures.

An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure

Structural Health Monitoring (SHM) is a very important research domain whereby civil infrastructure is monitored. Using wireless technologies can boost SHM by providing the level of autonomous operation that is essential for these tasks. Wireless routing, with its subset, geographic routing, is an important procedure that needs to be optimised, in order to lead packets to the basestation. Occasionally, routing is susceptible to interference and collisions due to a large number of connected devices. This fact led to cooperative transmission; cooperative networks are the ones that utilise relays to accomplish the transmission of packets; thus, resulting in link quality as well as throughput increase. In this paper, we investigate the Collection Tree Protocol (CTP) to show that it can be cooperative when used in an SHM for civil infrastructure monitoring applications giving a geographical essence to the routing protocol. We do that by exploiting the fact that the CTP’s mechanism uses its tree formation for a node to transmit to the best link quality parents. An example of a cooperative model to show that it may be applied to the protocol is given. Further, Indriya testbed results of direct and cooperative transmissions are given to strengthen the case of this work, with which a scenario where the CTP exhibits better link quality when using a relay is given. A practical addition is suggested, whereby an extra field in the packet struct is proposed, which will provide the CTP with further strength to changing conditions and direct communication loss.

Monitoring onsite‐temperature prediction error for condition monitoring of civil infrastructures

An inverse input–output method is proposed for long-term condition monitoring of civil infrastructures through monitoring the prediction error of air temperature recorded at the site of a structure. It is known that structural natural frequencies are affected by temperature. Hence, the proposed method considers the structural natural frequencies as input and temperature as output to train a machine learning algorithm (MLA). To this end, after signal preprocessing using the variational mode decomposition (VMD), different MLAs are employed, and the error associated with this prediction is regarded as damage–sensitive feature. It is hypothesised and further confirmed through solving numerical and benchmark problems that the prediction error deviates significantly from the upper bond control limit of an R-chart (errors signal) constructed based on the prediction error of temperature as soon as the damage occurs. The frequency–temperature scatter plots indicate a linear dependency between the natural frequencies and temperature. Moreover, the similar slope obtained for the regression line fitted to different frequency–temperature scatter plots indicates high collinearity among pairs of natural frequencies. This observation implies that an interaction term must be considered for such pairs of natural frequencies in the linear regression model. The results of both numerical and experimental studies further confirm that the interaction linear regression model is the most accurate machine learning algorithm for solving the inverse problem of predicting temperature using natural frequencies for condition monitoring of structures. The results of the proposed method are also compared with the direct strategy, whereby its superiority is demonstrated.

Experimental Validation of a High Precision GNSS System for Monitoring of Civil Infrastructures

In recent years, Global Navigation Satellite System (GNSS) technologies, which take full advantage of both real-time kinematic (RTK) and precise point positioning (PPP), managed to reach centimeter-level positioning accuracy with ambiguity resolution (AR) quick convergence techniques. One great advantage over traditional structural health monitoring (SHM) systems is that GNSS technologies will be functional in disaster management situations, when terrestrial communication links become unavailable. In this study, a multi-GNSS system, based on GPS and Galileo constellations and exploiting advanced RTK and PPP-AR technologies with update rate of 100 Hz is tested on two benchmark structures as an SHM system. The first case study served as a baseline to outline the methodology: first, a setup phase of the instrumentation, then a signal processing phase and last, the validation of the results. The methodology was then applied to a real-case scenario, in which the GNSS was tested on a road bridge. A comparative analysis with the results acquired by a set of accelerometers showed that the GNSS was able to identify the crossing of heavy vehicles. The work is paving the way for the development of an affordable and efficient multi-GNSS-based tool for the monitoring of civil infrastructures.

A Review of Vision-Laser-Based Civil Infrastructure Inspection and Monitoring.

Structural health and construction security are important problems in civil engineering. Regular infrastructure inspection and monitoring methods are mostly performed manually. Early automatic structural health monitoring techniques were mostly based on contact sensors, which usually are difficult to maintain in complex infrastructure environments. Therefore, non-contact infrastructure inspection and monitoring techniques received increasing interest in recent years, and they are widely used in all aspects of infrastructure life, owing to their convenience and non-destructive properties. This paper provides an overview of vision-based inspection and vision–laser-based monitoring techniques and applications. The inspection part includes image-processing algorithms, object detection, and semantic segmentation. In particular, infrastructure monitoring involves not only visual technologies but also different fusion methods of vision and lasers. Furthermore, the most important challenges for future automatic non-contact inspections and monitoring are discussed and the paper correspondingly concludes with state-of-the-art algorithms and applications to resolve these challenges.

Two-stage method based on the you only look once framework and image segmentation for crack detection in concrete structures

Detecting the presence of cracks and identifying their severity are crucial tasks for determining the structural health of a concrete building. In this study, we develop a two-stage automated method based on the You Only Look Once (YOLOv5) deep learning framework for the identification, localization, and quantification of cracks in the concrete structures. In the first stage, cracks are identified and localized using bounding boxes, while in the second stage, the length of cracks and, therefore, the damage severity are determined. The developed deep learning model is trained using 4500 annotated images from a total of 40000 images of size 227 × 227 pixel, which are obtained from an open-source dataset collected at various campus buildings of Middle East Technical University (METU). The concept of transfer learning (i.e., pre-trained weights) is used for the training, which drastically reduces the training time. The detection and localization accuracy of this model is measured in terms of the average precision, average recall, and F1-score. The YOLOv5 model achieves the mean average precision (mAP_0.5) of 95.02%. A ResNet model is also developed just to capture the supremacy of the YOLOv5 model. The proposed method can help in identifying structural anomalies through real-time monitoring that must be urgently repaired and thus can be used in high-quality civil infrastructure monitoring systems.

Remote Monitoring of Civil Infrastructure Based on TomoSAR

Structural health monitoring and damage detection tools are extremely important topics nowadays with the civil infrastructure aging and deteriorating problems observed in urban areas. These tasks can be done by visual inspection and by using traditional in situ methods, such as leveling or using traditional mechanical and electrical sensors, but these approaches are costly, labor-intensive and cannot be performed with a high temporal frequency. In recent years, remote sensing has proved to be a very promising methodology in evaluating the health of a structure by assessing its deformation and thermal dilation. The satellite-based Synthetic Aperture Radar Tomography (TomoSAR) technique, based on the exploitation of a stack of multi-temporal SAR images, allows to remotely sense the movement and the thermal dilation of individual structures with a centimeter- to millimeter-level accuracy, thanks to new generation high-resolution satellite-borne sensors. In this paper, the effectiveness of a recently developed TomoSAR technique in assessing both possible deformations and the thermal dilation evolution of man-made structures is shown. The results obtained using X-band SAR data in two case studies, concerning two urban structures in the city of Naples (Italy), are presented.

Numerical investigation of a bioinspired multi-segment soft pneumatic actuator for grasping applications

Soft robotic gripper is useful for grasping applications, such as pick-and-place tasks, post-disaster rescue and health monitoring of civil infrastructure. Inspired by the versatility of the human hand and bird claws, this paper designed a bioinspired actuator made of multi-segment fibre-reinforced pneumatic bending actuator and bellow-shape pneumatic rotational actuator joints. A numerical model was established to investigate the bending performance and stress distribution of the designed actuator under different input pressures. The influences of bottom layer thickness, chamber number at the actuator joints, and bending segment length on the overall bending performance of the designed soft actuator have been comprehensively investigated. In addition, the stress distribution of the actuator joints has been studied for understanding the mechanical performance of the actuator joints. Finally, a soft gripper made of two designed actuators has been demonstrated for grasping objects with different shapes and weights. The findings found in this investigation are helpful for the design and fabrication of robotic grasper, which can be further integrated on an industrial robotic arm and flying robotic platform for grasping and prosthetic hand applications.

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Damage assessment techniques based on entropy measurements have been recently proposed for the structural health monitoring of civil structures and infrastructures. A quasi-real-time approach, based on the use of instantaneous spectral entropy (ISE) over an uninterrupted stream of data, is discussed here. The methodology is proposed for the detection of sudden damage-related structural changes (more specifically, linear stiffness reductions and nonlinear breathing cracks). The method operates by framing the continuous stream of vibration signals and comparing the single frames to a known baseline. The approach is also suitable for nonstationary signals originating from nonlinearly behaving structures. The procedure is validated on an experimental benchmark: a laboratory-scaled model of a three-storey single-span frame metallic structure. Three different definitions of entropy and six candidate time–frequency/time-scale transforms have been tested to find the optimal settings.

XShake : Intelligent wireless system for cost-effective real-time seismic monitoring of civil infrastructure

Seismic structural health monitoring (SHM) of structures is critical not only to detect earthquakes to send early warning, but also to enable rapid structural condition assessment to ensure safety. Traditional monitoring systems using wired sensors are expensive. Wireless sensors offer tremendous opportunity to reduce costs, which remains elusive for seismic structural monitoring due to two main obstacles. First, there are constraints on power resources. Most wireless sensors are dutycycled to preserve limited battery power; and hence, can miss an earthquake in power-saving sleep mode. Second, there is a lack of support for rapid post-event data collection and processing. Conventional data transmission after sensing can introduce significant delays, and real-time data acquisition that eliminates these delays has limited throughput. In this paper, an intelligent wireless monitoring system, xShake, is developed for cost-effective real-time seismic SHM. It consists of: 1) energy-efficient wireless sensor prototypes utilizing on-demand sensing technique, 2) live-streaming framework that supports high-throughput real-time data acquisition, and 3) a rapid condition assessment application, enabling real-time data visualization and processing for end users. The performance of the xShake is validated through lab tests, demonstrating that it can capture high-fidelity synchronized data under earthquakes and enable real-time structural condition assessment.