Search

Aging water transmission systems in the United States urgently require more efficient approaches to risk management to ensure that systems can operate without failure. Water utilities with spatially vast transmission systems collect extensive data related to the operation of the system, including internal fluid pressures. Combined with representative models that describe both the behavior and condition of the transmission system pipes, a reliability framework can be used to quantify the probability of failure of pipe segments. This work adopts transient pressure data to develop a statistical model of the maximum internal fluid pressures present in a transmission system. This data is combined with an analytical multi-layered ring model of the system pipes to estimate the system reliability. The study adopts the transmission system of the Great Lakes Water Authority (GLWA) to illustrate the use of the reliability framework to estimate the probability of failure of prestressed concrete cylinder pipe (PCCP) segments which have previously experienced failures in many systems across the US.

This paper uses long-term bridge monitoring data to quantitatively assess the composite action exhibited in slab-on-girder highway bridges and investigates the potential relationship betwee...

Physical activities and social interactions are essential activities that ensure a healthy lifestyle. Public open spaces (POS), such as parks, plazas and greenways, are key environments that encourage those activities. To evaluate a POS, there is a need to study how humans use the facilities within it. However, traditional approaches to studying use of POS are manual and therefore time and labor intensive. They also may only provide qualitative insights. It is appealing to make use of surveillance cameras and to extract user-related information through computer vision. This paper proposes a proof-of-concept deep learning computer vision framework for measuring human activities quantitatively in POS and demonstrates a case study of the proposed framework using the Detroit Riverfront Conservancy (DRFC) surveillance camera network. A custom image dataset is presented to train the framework; the dataset includes 7826 fully annotated images collected from 18 cameras across the DRFC park space under various illumination conditions. Dataset analysis is also provided as well as a baseline model for one-step user localization and activity recognition. The mAP results are 77.5\% for {\it pedestrian} detection and 81.6\% for {\it cyclist} detection. Behavioral maps are autonomously generated by the framework to locate different POS users and the average error for behavioral localization is within 10 cm.

Osseointegrated prosthesis is essentially a prosthetic fixture surgically implanted into the bone that extends out of the limb so that an artificial limb can be attached. While osseointegrated prostheses can dramatically improve the quality of life of amputees, there remains a lack of quantitative evidence of the osseointegration process that occurs at the bone–prosthesis surface after surgery. This study advances a sensing strategy that employs piezoelectric elements mounted to the percutaneous end of the prosthesis to generate guided waves that propagate along the length of the prosthesis fixture. The properties of the guided waves exhibit sensitivity to both the degree of bone healing that occurs at the prosthesis surface and the movement of the prosthesis due to loss of osseointegration. Use of the prosthesis as a wave guide offers care providers a quantitative approach to determining when an osseointegrated prosthesis can be loaded and tracks the integrity of osseointegration over the lifespan of the amputee. The study validates the proposed guided wave strategy using a prosthesis model consisting of a solid titanium rod implanted in an adult femoral bone. First, a high-fidelity finite element model is created to study changes in guided waves as a result of bone healing. A laboratory model is also adopted using a synthetic femoral bone identical to that modeled in the finite element model. The energy of the first longitudinal wave mode introduced at the percutaneous end of the prosthesis provides a repeatable metric for accurate assessment of both osseointegration and prosthesis pullout from the bone. The results of this study reveal that the energy of the longitudinal wave mode decreases by nearly half during the osseointegration healing process. In addition, the wave energy is also found to increase as the osseointegrated fixture loosens and is withdrawn from the bone.

Thousands of landslides occurred during the April 2015 Gorkha earthquake in Nepal. Previous work using satellite imagery mapped nearly 25,000 coseismic landslides. In this study, the satellite-based mapping was analyzed in three areas where field deployment was also conducted—the Budhi Gandaki, Trishuli, and Indrawati river valleys—to better characterize the landslides. Unmanned aerial vehicles (UAVs) were deployed to map the three-dimensional (3-D) geometry of failed slopes using photogrammetry, as well as to characterize rock structure and strength. The majority of landslides were rock slides along the ridges and the steeper portions of the basins primarily involving the weathered rock zone. Additional landslides included rock falls and soil failures. Satellite imagery analysis indicated that landsliding was concentrated north of the physiographic transition, in steep areas, and in close proximity to the major rivers. The Trishuli area experienced the lowest landslide density in terms of number of landslides compared to the Budhi Gandaki and Indrawati areas, although all three areas had similar density in terms of total landslide area and other landslide statistics.

This paper proposes a wireless hull monitoring system that is quick to install as a short-term monitoring solution. Hull measurements have the potential to increase the accuracy of ship response predictions at a lower cost than computer simulation or towing tank models. The performance of the wireless monitoring system is validated on the all-aluminium United States Coast Guard Response Boat-Medium. The system is designed to measure ship motions and hull strain responses during high-speed operations and in harsh weather conditions. An analytical framework is developed to extract sea states from inertial measurements recorded at the ship centre-of-gravity and in a bow compartment. To assess the fatigue life of the hull during harsh weather operations, response amplitude operators (RAOs) are empirically derived to map sea states to root mean square accelerations and strain cycles measured from a high-stress hull element. A RAO that maps sea state to consumed fatigue in the hull, so termed a consumed...

This paper presents the results of three full-scale experiments performed on segmental concrete pipelines subjected to permanent ground displacement. The first pipeline was made of reinforced concrete pipes and the second pipeline was made of steel fibre-reinforced concrete pipes. The third pipeline was made of a combination of fibre-reinforced and reinforced concrete pipes. An array of sensing techniques was used to assess the damage evolution in pipelines and their overall performance. Three stages of damage were observed. In the first stage, damage was concentrated in the joints near the fault line. In the second stage, the damage occurred in all joints along the pipeline. While in the first two stages damage was mainly concentrated at the bell and spigot joints of the pipe segments, the third stage of damage was characterised by severe damage and rupture of the body of pipe segments located in the immediate vicinity of the fault line. The modes of failure for the plain and fibre-reinforced con...

This chapter provides an overview of a Cyber Physical System (CPS) for civil infrastructural monitoring. Specifically, a prototype design and implementation of a cyber infrastructure framework for the monitoring of bridges along a highway corridor is described. The cyber infrastructure framework includes two basic components, namely a sensing and monitoring component and a cloud-based computational platform. The sensing and monitoring components includes a network of sensors and cameras instrumented along the highway corridor to capture vehicle loads and bridge responses. The computational tasks involve information modeling, database management and web services for supporting SHM applications. Selected examples are provided to illustrate the utilization of the CPS for assessing the fundamental behaviors of bridge structures. Additionally, the CPS provides a platform that enables research and development of new and innovative data-driven approaches for SHM.

Naval ships can be exposed to munition detonations during their operations in theaters of engagement. Munition blasts can be devastating to the ship structure as well as to the crew within the ship. Current understanding of the impact of blast loads on ships is based on empirical evidence from operational events that have inflicted casualties and injured sailors; there are comparatively fewer examples of controlled blast experiments on actual ships. Controlled blast testing on ships could advance understanding of how blast pressures propagate within the ship interior and the impact of these loads on ship crews. This study focuses on the development of a self-sufficient wireless monitoring system that can be used to monitor ship structures and the response of ship crews during blast events. The proposed system is designed to be deployed on decommissioned, crewless naval ships used as targets during naval exercises. The system consists of two parts. The first is a stationary shipboard monitoring system installed on the ship to monitor the ship motions and to capture ship vibrations and internal pressures during a blast event. The stationary monitoring system is powered entirely by solar energy and includes a GPS receiver to monitor ship locations and a satellite modem for the communication of data. The stationary system includes wireless sensors installed on the ship structure that trigger to capture ship vibrations and internal compartment pressures just prior to and during the blast event. The second part of the shipboard monitoring system are mannequins instrumented with wireless sensors to measure and transmit mannequin motion during blast loading. This paper describes the design of the monitoring solution and its deployment on a decommissioned ship exposed to blast loads during naval exercises. The result from sea trials reveal a stable and self-sufficient monitoring system that is survivable to the blast event.

While infrastructure has historically been the primary focus of resilience assessments, resilient societies are not only a function of physical systems. Urbanization and climate change are unquestionably posing grand challenges that will affect the performance of physical systems. However, they are also imposing new stressors on social systems that may change the ways in which society uses infrastructure. Consequently, true resilience can only be achieved if people and their actions are fully accounted for. This paper presents two new data collection approaches to quantitatively assess human actions in public urban spaces as a preliminary effort to sense and model social systems. Passive infrared sensors integrated into wireless sensing nodes are used to collect anonymous, discrete event data characterizing foot traffic, and security camera video feeds used as continuous data input for computer vision processing. This paper gives an overview of the data collection program and the results from deployments at the Detroit Riverfront. These data sources are used to identify people, quantify their mobility, and classify their activities.

Unmanned aerial vehicles (UAV), or drones, have become popular tools for practitioners and researchers alike. Recent years have seen a significant increase in UAV uses for many applications...

Highways consist of many large structures requiring vigilant inspection and maintenance. While significant research attention has been focused on the health management of bridges, comparatively less attention has been paid to other highway structures including retaining walls. In the United States, there has been a recent emphasis on extending the use of risk management methods to the extremely large national inventory of retaining wall structures. In this paper, a long-term wireless monitoring system is developed as a cost-efficient approach to collecting data and information required for risk assessment of retaining wall structures. The study focuses on two reinforced concrete (RC) retaining walls to highlight the monitoring system design and to illustrate how measurement data offers insight to wall performance. The first wall is a caisson supported retaining wall along the M-10 freeway in Detroit, MI; the second is a classical reinforced concrete cantilever wall along I-696 in Southfield, MI. The wireless monitoring system installed on each wall system uses a cellular-based wireless sensor node termed Urbano that is solar powered and customized to measure wall tilt using inclinometers, wall strain using strain gages, and wall temperature using thermistors. The monitoring systems have been valuable in assessing the behavior of the M-10 and I-696 wall systems for a broader risk management framework. The monitoring results reveal both wall systems are operating as designed with limited tilt and strain responses to normal environmental factors including moisture and temperature.

The widespread availability of cost-effective sensing technologies is translating into an increasing number of highway bridges being instrumented with structural health monitoring (SHM) systems. Current bridge SHM systems are only capable of measuring bridge responses and lack the ability to directly measure the traffic loads inducing bridge responses. The output-only nature of the monitoring data available often leaves damage detection algorithms ill-posed and incapable of robust detection. Attempting to overcome this challenge, this study leverages state-of-the-art computer vision techniques to establish a means of reliably acquiring load data associated with the trucks inducing bridge responses. Using a cyberenabled highway corridor consisting of cameras, bridge monitoring systems, and weigh-in-motion (WIM) stations, computer vision methods are used to track trucks as they excite bridges and pass WIM stations where their weight parameters are acquired. Convolutional neural network (CNN) methods are used to develop automated vehicle detectors embedded in GPU-enabled cameras along highway corridors to identify and track trucks from real-time traffic video. Detected vehicles are used to trigger the bridge monitoring systems to ensure structural responses are captured when trucks pass. In the study, multiple one-stage object detection CNN architectures have been trained using a customized dataset to identify various types of vehicles captured at multiple locations along a highway corridor. YOLOv3 is selected for its competitive speed and precision in identifying trucks. A customized CNN-based embedding network is trained following a triplet architecture to convert each truck image into a feature vector and the Euclidean distance of two feature vectors is used as a measure of truck similarity for reidentification purposes. The performance of the CNN-based feature extract is proved to be more robust than a hand-crafted method. Reidentification of the same vehicle allows truck weights measured at the WIM station to be associated with measured bridge responses collected by bridge monitoring systems.

If you have been following the news, browsing the internet, or even gazing up in the sky, it is likely that you have seen an Unmanned Aerial Vehicle (UAV), also commonly known as Unmanned Aircraft ...

This special issue provides readers with a picture of the current state of the art in the structural health monitoring field while highlighting the new research avenues that are being aggressively explored.

This paper proposes a three-tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and operational conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-tier modular framework are presented and applied to acceleration and EOC data collected from an operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.

The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and ...

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.

Osseointegration of a prosthesis offers a novel approach to enhancing the quality of life of an amputee because it makes an artificial limb an integral part of their body. While osseointegrated prostheses offer amputees many benefits, long-term health of the prosthesis fixture in the host bone is a concern. In particular, overloading of the fixture can result in damage to the host bone including bone fracture. This study offers a novel sensing strategy implemented on the percutaneous end of an osseointegrated prosthesis. Piezoelectric actuators are used to generate elastic stress waves in the prosthesis to interrogate the integrity of the prosthesis-bone interface. In this study, flexural mode Rayleigh waves are introduced in the prosthesis to identify the existence and location of fracture in the host bone. A prosthetic model consisting of a titanium rod implanted in a synthetic sawbone with piezoelectric wafer elements bonded to the rod surface is used to validate the proposed approach. The work reveals the waveforms associated with flexural wave modes are directly correlated to bone fracture occurring at the prosthesis-bone interface with fracture location identifiable in the reflect wave features.

Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

Buried lifeline infrastructure including pipelines, tunnels, power and communication lines, among others, are vital to ensuring the operation of the national economy. Permanent ground displacement (PGD) from earthquakes and landslides is the most serious hazard to buried pipelines, prompting often slow and expensive methods of damage localization before repairs can be made. Due to the importance of these buried lifelines, it is critical that low-cost and rapid methodologies for damage detection and localization be developed. Monitoring systems embedded in and around the pipeline are an obvious approach but typically suffer from the cost and obtrusiveness of long cable requirements. The primary goal of this chapter is to illustrate novel sensing methods that can serve as the basis for monitoring buried pipelines exposed to PGD. In particular, the chapter focuses on the monitoring of segmented concrete pipelines, which typically experience damage at their joints due to PGD. Wireless telemetry is evaluated to validate wireless sensors for buried applications, thus reducing greatly the cost of dense sensor systems in regions of high PGD risk. An overview of current buried pipeline sensing technology is made and three experimental full-scale PGD tests are conducted to evaluate pipeline motion and damage detection methodologies in segmented concrete pipelines. Real-time monitoring of joint rotations and translations by potentiometers as well as direct damage measures of joint regions by acoustic emission and conductive surface sensors were made. Strain gages were used to successfully portray global load transfer throughout the pipeline, validated by load cell measurements at the pipe ends. The combined sensor information is successfully used to create a hypothesis for the damage evolution process of buried segmented concrete pipelines under PGD and to validate the use of wireless sensors for buried pipeline monitoring.

In SHM applications various damage-sensitive features can be used for making decisions regarding damage detection. In all cases, classifiers evaluate the results and make a final decision regarding the state of the structure. Often, there are discrepancies among the decisions of different classifiers, resulting in different detection performances for each damage feature. This is expected as different classifiers may be better suited for different data settings, even in data sets corresponding to the same system. Boosting algorithms combine multiple base classifiers to produce an ensemble, whose joint decision offers a better performance than any of the base classifiers. Adaptive Boosting (AdaBoost) is deployed in this paper to build a strong classifier based on the classifiers of a three-tier modular SHM framework for improving detection performance. The framework consists of three parts: application of machine learning clustering algorithms for data normalization, feature extraction and hypothesis testing (HT). Each connection of damage feature, also referred to as condition parameter (CP), and HT composes a classifier that can be used as a weak classifier in the boosting algorithm. Information from the SHM framework classifiers is used, in order to build a strong classifier that is able to classify the value of any CP and improve the detection performance. The integration of AdaBoost with the three-tier SHM framework is validated on an operating 3 kW wind turbine. The results are demonstrated in receiver operating characteristic (ROC) curves with AdaBoost increasing the performance of damage detection.

A novel d36-type piezoelectric wafer fabricated from lead magnesium niobate-lead titanate (PMN-PT) is explored for the generation of in-plane horizontal shear waves in plate structures. The study focuses on the development of a linear phased array (PA) of PMN-PT wafers to improve the damage detection capabilities of a structural health monitoring (SHM) system. An attractive property of in-plane horizontal shear waves is that they are nondispersive yet sensitive to damage. This study characterizes the directionality of body waves (Lamb and horizontal shear) created by a single PMN-PT wafer bonded to the surface of a metallic plate structure. Second, a linear PA is designed from PMN-PT wafers to steer and focus Lamb and horizontal shear waves in a plate structure. Numerical studies are conducted to explore the capabilities of a PMN-PT-based PA to detect damage in aluminum plates. Numerical simulations are conducted using the Local Interaction Simulation Approach (LISA) implemented on a parallelized graphical processing unit (GPU) for high-speed execution. Numerical studies are further validated using experimental tests conducted with a linear PA. The study confirms the ability of an PMN-PT phased array to accurately detect and localize damage in aluminum plates.

This paper describes a cloud-based cyberinfrastructure framework for the management of the diverse data involved in bridge monitoring. Bridge monitoring involves various hardware systems, software tools and laborious activities that include, for examples, a structural health monitoring (SHM), sensor network, engineering analysis programs and visual inspection. Very often, these monitoring systems, tools and activities are not coordinated, and the collected information are not shared. A well-designed integrated data management framework can support the effective use of the data and, thereby, enhance bridge management and maintenance operations. The cloud-based cyberinfrastructure framework presented herein is designed to manage not only sensor measurement data acquired from the SHM system, but also other relevant information, such as bridge engineering model and traffic videos, in an integrated manner. For the scalability and flexibility, cloud computing services and distributed database systems are employed. The information stored can be accessed through standard web interfaces. For demonstration, the cyberinfrastructure system is implemented for the monitoring of the bridges located along the I-275 Corridor in the state of Michigan.

The ability to quantitatively assess the condition of railroad bridges facilitates objective evaluation of their robustness in the face of hazard events. Of particular importance is the need to assess the condition of railroad bridges in networks that are exposed to multiple hazards. Data collected from structural health monitoring (SHM) can be used to better maintain a structure by prompting preventative (rather than reactive) maintenance strategies and supplying quantitative information to aid in recovery. To that end, a wireless monitoring system is validated and installed on the Harahan Bridge which is a hundred-year-old long-span railroad truss bridge that crosses the Mississippi River near Memphis, TN. This bridge is exposed to multiple hazards including scour, vehicle/barge impact, seismic activity, and aging. The instrumented sensing system targets non-redundant structural components and areas of the truss and floor system that bridge managers are most concerned about based on previous inspections and structural analysis. This paper details the monitoring system and the analytical method for the assessment of bridge condition based on automated data-driven analyses. Two primary objectives of monitoring the system performance are discussed: 1) monitoring fatigue accumulation in critical tensile truss elements; and 2) monitoring the reliability index values associated with sub-system limit states of these members. Moreover, since the reliability index is a scalar indicator of the safety of components, quantifiable condition assessment can be used as an objective metric so that bridge owners can make informed damage mitigation strategies and optimize resource management on single bridge or network levels.

To provide passive heat storage in buildings, materials exhibiting a phase-change within building operating temperature can be incorporated into the envelope material. This study assesses the viability of incorporating a paraffin phase change material (PCM) into an Engineered Cementitious Composite (ECC) because the tensile ductility of ECC allows formation of thin panels—a favorable geometry for building facades. Inclusion of 3% PCM by mass provided a 40% increase in ECC specific heat capacity at phase change temperature while maintaining a 28 MPa compressive strength and 4% tensile strain capacity on average.

Advances in energy harvesting systems are needed to power wireless sensors for structural health monitoring. Research on developing a harvesting system that converts the low frequency, non-periodic, and low-acceleration vibrations present on bridges is continued and significantly extended in this work. The mechanics of the harvester were optimized to increase its robustness and lifetime, power electronics were added, and the complete system was installed on the New Carquinez suspension bridge in California. The complete results and analysis are presented in this study. The power management circuit is added to rectify and boost the low AC output of the harvester and convert it into a usable DC voltage. The harvester design is further enhanced to significantly improve performance and robustness. During short-term on-bridge testing, the system was able to charge a 10 μF capacitor to 2 V DC, and the average harvester output power ranges from 1.6 to 5.0 μW, depending on the location on the bridge, a 10× improvement over previous results. A long-term test of the harvesting system has been conducted, during which the performance of the system was monitored remotely using a wireless sensor network. The system improvements described in this study enabled continuous operation in the harsh bridge environment for 13 months starting April 30, 2012 and constitute a major milestone in the development of miniaturized motion harvesters. Finally, the system was retrieved and analyzed to understand and verify the cause of observed long-term performance changes.

Engineered Cementitious Composites (ECCs) are structural materials known for their excellent tensile ductility and damage tolerance. Previous experimental studies have shown a strong dependence of electrical resistivity of ECC on applied mechanical tensile strain (piezoresistive behavior), which can be potentially utilized for self-sensing mechanical damage for structural health monitoring. In this paper, the influence of micro-cracks on the composite electrical response of ECC under direct tension is investigated experimentally as well as analytically. For this purpose, the electrical–mechanical properties of two ECCs with different crack patterns are compared at macro (composite) and meso (single-crack) scales. An analytical model linking single-crack electrical response and crack pattern of an ECC to its composite electrical behavior is proposed in this study, and verified for both ECCs with experimental observations. Thus, a fundamental understanding of crack patterns and their effects on piezoresistivity of ECC is developed in this study.

While wireless sensor networks have been successfully deployed on a variety of civil infrastructure systems for structural monitoring, past studies have shown that there is room for improvement in terms of network robustness and overall resource consumption efficiency. The mechanisms employed by biological nervous systems (e.g. signal modulation, communication, and integration) can be used as inspiration for overcoming the performance bottlenecks seen in existing wireless sensor nodes and networks. The mammalian auditory system is of particular interest due to its unique signal decomposition techniques (performed by the cochlea) that enable real-time processing of complex sound signals. In this article, a novel wireless sensor architecture based on the operational principles of cochlea is described. The performance of the proposed sensor is validated on a single-degree-of-freedom structure that is excited by seismic ground motion signals, thus demonstrating its real-time monitoring capabilities while maintaining high data compression rates.

Dense networks of low-cost wireless sensors have the potential to facilitate prolific data collection in large and complex infrastructure at costs lower than those historically associated w...

This article presents the implementation of a calibration procedure for a finite element (FE) model of a state highway bridge using sensory data measured on the bridge. The objective is to modify the high-fidelity FE model of the bridge so that its dynamic behaviour matches, as closely as possible, that of the bridge under analysis. The bridge under investigation is a steel–concrete composite bridge that is instrumented with a wireless monitoring system to collect its vibration response under ambient vibrations. A detailed three-dimensional FE model of the bridge was developed to represent the bridge as realistically as possible. The detailed modelling can minimise the amount of uncertainty in the model and the number of parameters that require updating. A multi-variable sensitivity-based objective function is used to minimise the error between the experimentally measured and the FE-computed modal characteristics. An iterative optimisation approach has been undertaken to find the optimum structural parame...

The unmanned aerial vehicle (UAV) is a light weight flight system that can carry sensors and cameras for data collection. Tremendous excitement surrounds the use of UAVs because they can be deployed easily and rapidly for data collection; they also can be programmed to execute missions with high degrees of autonomy. For these reasons, UAVs hold promise in accelerating the collection of data in geophysical explorations. In this study, a UAV platform is explored for the collection of data from geophones deployed to measure the vibrations of a concrete slab foundation. The UAV is designed to drop a weight as a controlled energy source. Both the energy and location of the impulsive load are adjustable by the flight parameters of the UAV. The study adopts a time-domain analysis for source localization using the dense array of geophones

Unmanned aerial vehicles (UAVs) can serve as a powerful mobile sensing platform for assessing the health of civil infrastructure systems. To date, the majority of their uses have been dedicated to vision and laser-based spatial imaging using on-board cameras and LiDAR units, respectively. Comparatively less work has focused on integration of other sensing modalities relevant to structural monitoring applications. The overarching goal of this study is to explore the ability for UAVs to deploy a network of wireless sensors on structures for controlled vibration testing. The study develops a UAV platform with an integrated robotic gripper that can be used to install wireless sensors in structures, drop a heavy weight for the introduction of impact loads, and to uninstall wireless sensors for reinstallation elsewhere. A pose estimation algorithm is embedded in the UAV to estimate the location of the UAV during sensor placement and impact load introduction. The Martlet wireless sensor network architecture is integrated with the UAV to provide the UAV a mobile sensing capability. The UAV is programmed to command field deployed Martlets, aggregate and temporarily store data from the wireless sensor network, and to communicate data to a fixed base station on site. This study demonstrates the integrated UAV system using a simply supported beam in the lab with Martlet wireless sensors placed by the UAV and impact load testing performed. The study verifies the feasibility of the integrated UAV-wireless monitoring system architecture with accurate modal characteristics of the beam estimated by modal analysis.

This paper describes an information repository to support bridge monitoring applications on a cloud computing platform. Bridge monitoring, with instrumentation of sensors in particular, collects significant amount of data. In addition to sensor data, a wide variety of information such as bridge geometry, analysis model and sensor description need to be stored. Data management plays an important role to facilitate data utilization and data sharing. While bridge information modeling (BrIM) technologies and standards have been proposed and they provide a means to enable integration and facilitate interoperability, current BrIM standards support mostly the information about bridge geometry. In this study, we extend the BrIM schema to include analysis models and sensor information. Specifically, using the OpenBrIM standards as the base, we draw on CSI Bridge, a commercial software widely used for bridge analysis and design, and SensorML, a standard schema for sensor definition, to define the data entities necessary for bridge monitoring applications. NoSQL database systems are employed for data repository. Cloud service infrastructure is deployed to enhance scalability, flexibility and accessibility of the data management system. The data model and systems are tested using the bridge model and the sensor data collected at the Telegraph Road Bridge, Monroe, Michigan.

New advances in nanotechnology and material processing is creating opportunities for the design and fabrication of a new generation of thin film sensors that can used to assess structural health. In particular, thin film sensors attached to large areas of the structure surface has the potential to provide spatially rich data on the performance and health of a structure. This study focuses on the development of a fully integrated strain sensor that is fabricated on a flexible substrate for potentially use in sensing skins. This is completed using a carbon nanotube-polymer composite material that is patterned on a flexible polyimide substrate using optical lithography. The piezoresistive carbon nanotube elements are integrated into a complete sensing system by patterning copper electrodes and integrating off-the-shelf electrical components on the flexible film for expanded functionality. This diverse material utilization is realized in a versatile process flow to illustrate a powerful toolbox for sensing severity, location, and failure mode of damage on structural components. The fully integrated patterned carbon nanotube strain sensor is tested on a quarter-scale, composite beam column connection. The results and implications for future structural damage detection are discussed.

Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio-inspired control architecture and finds that it does not significantly impact the effectiveness of control.