This PDF file contains the front matter associated with SPIE Proceedings Volume 11382, including the Title Page, Copyright information, and Table of Contents.

Gas turbine engine manufacturers are in continuous strive to improve the durability and the technology behind engine development to help monitor engine health and performance. Such technologies are confined to employing highly specialized sensors within the engine compartment. The role of the sensors is to screen and track the structural response of the engine components and in particular the rotor disk due to its venerability to endure failure since it is subject to complex and harsh loading conditions. Detecting unexpected or excessive blade vibration before failure is critical to ensure safety and to achieve projected component life. Nondestructive Evaluation has been the traditional method of detection in addition to relying exclusively on visual inspections as well as other means. These methods require time and cost and do not provide accurate feedback on the health when the engine is in operation. At NASA Glenn Research Center, efforts are undergoing to develop, and test validates microwave-based blade tip timing sensors in support of these concerns and to investigate their application for propulsion health monitoring under the Transformational Tools and Technologies Project (TTTP). A set of prototype sensors is used to assess their ability and applicability in making blade tip clearance measurements in an attempt to extract the blade tip timing from the acquired raw data. The sensors are non-contact type and microwavebased technology. The study covers an experimental task to define the optimum set-up of these sensors, determine their sensitivity in making blade tip deflection measurements and validate their performance against realistic geometries in a spin rig. It also includes finite element analysis base calculations to compare with the experimental data. Data pertaining to the findings obtained from the testing as well as the analytical results are presented and discussed. This work is an extension of a prior combined experimental and computational study that is available in reference [1].

A new computer vision-based method is proposed for concrete crack detection in tunnel structures using multi-spectral dynamic imaging (MSX). The MSX images were collected from a tunnel in the University of Manitoba, Canada. A total of 3600 MSX images (299 × 299 pixels) were used to train the modified deep inception neural network (DINN), and an additional 300 MSX images (299 × 299 pixels) were employed for validation purposes. The MSX images were examined by the trained neural network for concrete crack detection. The main purpose of this research was to examine the potential of the neural network to distinguish between noise and concrete surface cracks in the MSX images. A fully connected layer and a softmax layer were added to the DINN network in the transfer learning section to reduce the network computation cost. The proposed network used green bounding boxes to detect the portions with cracks in the MSX images. A training accuracy of 95.5% and a validation accuracy of 94% were achieved at 1600 iterations. The optimum training steps obtained from the training and validation were used for testing purposes. The robustness of the trained network was evaluated using an additional 96 MSX images (640 × 480 pixels). A maximum testing accuracy of 94% was recorded when the prediction probability was limited to 90%.

Detection of high energy laser strikes is key to the survivability of military assets in future warfare. The introduction of laser weapon systems demands the capability to quickly detect these strikes without disrupting the stealth capability of military craft with active sensing technologies. This paper explores the use of thermoelectric generators (TEGs) as selfpowered passive sensors to detect such strikes. Experiments were conducted using lasers of various power ratings, wavelengths, and beam sizes to strike 2cm x 2cm commercially available TEGs arranged in different configurations. Open circuit voltage and short circuit current responses of TEGs struck with 808nm, 1070nm, and 1980nm lasers at irradiance levels between 8.5-509.3W/cm² and spot sizes between 2-8mm are compared. TEG surface temperatures indicate that the sensor can survive temperatures nearing 400°C. TEG open circuit voltage magnitudes correlate more strongly with net incident laser power than with specific irradiance levels, and linearity is limited by Seebeck coefficient variation with temperature. Open circuit voltage responses are characterized by 10%-90% rise times of ~2-10s, despite surface temperatures not reaching equilibrium. With open circuit voltage as the sensing parameter, detection thresholds three times the above the standard deviation noise level can be exceeded within 300ms of the start of a laser strike with irradiance levels of ~200W/cm². Potential harvested power levels as high as 16mW are estimated based on measured electrical responses. A multi-physics finite element model corresponding to the experiments was developed to further optimization of a lightweight, low-profile TEG sensor for detection of high energy laser strikes.

Condition assessment of underground buried utilities, especially water distribution networks, is crucial to the decision making process for pipe replacement and rehabilitation. Hence, regular inspection of the water pipelines is carried out with in-pipe inspection robots to assess the internal condition of the water pipelines. However, the inspection robots need to identify and negotiate with the valves to pass through. Therefore, the aim of this study is to detect the valves in water pipelines in real-time to ensure smooth operation of the inspection robot. In this paper, four state-of-the-art deep neural network algorithms namely, Faster R-CNN, RFCN, SSD, and YOLO are presented to perform the real-time valve detection analysis. The study shows that Faster R-CNN, pre-trained with Resnet101 outperforms all the selected models by achieving 97:35% and 76:73% mean Average precison (mAP) values when the threshold for prediction is set to 50% and 75% respectively. However, in terms of the detection rate in frames per second (FPS), YOLOv3-608 seems to have better processing speed than all other models.

Nonlinear ultrasonic methods for non-destructive evaluation and damage detection rely on the measurement of nonlinear elastic effects, such as amplitude of the second harmonic in the frequency response of the sample, to reveal the presence of surface and internal cracks of various scale and nature. These methods require an amplification system and a high sensitivity ultrasonic transducer to measure nonlinear features, since harmonics are typically an order of magnitude lower than the fundamental frequency. In this work, we investigated various geometrical filters to amplify nonlinear signals and improve nonlinear air-coupled inspections: hyperbolic, cylindrical and conical duct. A 40 kHz ultrasonic speaker and standard Air Coupled ultrasound system arranged with 88 transmitting elements and 1 receiving element were used to conduct the experimental test. The results show that the passive hyperbolic-shaped filter was able to increase the second harmonic response of the damage region of 5db, compared to standard nonlinear inspections, and increases the signal to noise ratio of the measured signal of 11 db. Results shows that higher harmonics generated from instrumentation highly decrease as the wave propagates through the converging horn. Air-coupled inspection confirms the increase at the damage location of the second harmonic of the wave propagating though the diverging horn. The proposed setup could allow more accurate nonlinear air-coupled inspection of complex materials.

X-ray Computed Tomography has gained popularity as a metrology technique for components with detailed internal features. However achieving micron-scale resolution using X-ray CT is challenging. Synchrotron X-ray sources are highly collimated and brilliant, allowing high resolution tomography in metallic components. The X-ray Fuel Spray research at Argonne National Lab is aimed at utilizing synchrotron X-ray diagnostics for providing insights into automotive fuel injection. We present a case study of micro-CT for automotive fuel injectors, with orifices smaller than 100 micrometers. These orifices are imaged with 1 micrometer voxel size with minimal resolvable features of 2 micrometer. Tomographic analysis on large datasets must preserve resolution while being computationally efficient, which is facilitated by deep learning techniques for segmentation developed in-house. The accuracy of segmentation is evaluated using synthetic data. As results, we show high-quality iso-surface extraction and measurements of orifice features for 7 identical fuel injectors, indicating the extent of manufacturing variability. The capabilities developed in our team have potential applicability in the field of metal additive manufacturing, specifically for millimeter-sized components with micron-scale features.

There is a need for embedded sensor technologies to monitor wellbore integrity in real-time for carbon storage and geothermal applications. Emerging sensing technologies such as optical fiber sensors and wireless sensors have been studied for physical parameter monitoring (e.g. temperature, vibration, and strain) and chemical parameter monitoring (e.g. pH, CO₂, corrosion) to monitor structural health of the wellbore. The desirable sensors need to be able to withstand the harsh environments relevant for carbon storage and geothermal wellbores, and they must not inadvertently cause potential sources of wellbore failures. Therefore, we investigated the cement properties with embedded sensors to compare with baseline cement properties, including porosity, permeability, mechanical properties (e.g. Young’s modulus, Poisson’s Ratio, etc), and 3D computed tomography (CT) scans. The sensor devices (optical fiber sensors [OFS] and wireless chip sensors) were embedded in cement cores under wellbore relevant conditions. Then, the cement samples were examined using AutoLab 1500, nitrogen permeability testing, helium porosity testing, and 3D CT scanners. Results show that the cement samples with embedded sensor devices had a slight increase in porosity of 1.5% to 3.6% compared to the blank cement samples. Permeability slightly increased by 0.001 mD with embedded chip sensors. The embedded chip sensors did not significantly change the cement mechanical properties; whereas, the embedded OFS prototypes improved the cement mechanical strengths, e.g. increasing the Young’s modulus by as much as 10% and the bulk modulus by up to 25.5%. CT scans confirmed the proper embedding and good bonding between sensor devices and cement.

Fundamental research to understand changes in piezoelectric properties under irradiation are required if ultrasonic sensors are to be designed and deployed for nuclear reactor in-core measurements. Previous research has examined the survivability of bismuth titanate (BiT)-based ultrasonic sensors to a total dose of up to ~1021 neutrons/cm2. This paper describes efforts to quantify the changes in piezoelectric properties in these materials using piezoresponse force microscopy (PFM). PFM measurements from non-irradiated and irradiated specimens indicated a decrease in d33 from irradiation, consistent with the observed decrease in the response of a BiT transducer over the course of the irradiation test.

In vibration energy harvesting systems, mechanical damping is reduced to minimum to get large electrical power output. However, small mechanical damping will result in a narrow bandwidth in the frequency domain. This will lead to non-ideal performance when the excitation frequency does not match with the natural frequency. In human body energy harvesting backpack, the human comfort will also have to take into consideration besides harvesting power. In this paper, we proposed an electrical damping tuning method for energy harvesting backpack. The electrical damping will change according to the excitation frequency to achieve high power output at lower frequency. In this way, the frequency bandwidth increases. At resonance and high frequency, the damping will be tuned to control the maximum stroke of the backpack. This will make the wearer feel comfortable while substantial power is harvested. The electrical damping tuning circuit senses the input frequency and tunes the electrical damping accordingly. The circuit design and the control strategy are described in detail. Experiment will be done to validate the design goals.

This paper introduces a creative metamaterial-based substrate (MetaSub) for piezoelectric energy harvesters. The MetaSub is a platform with a high flexibility in both longitudinal and transverse directions. The novel design of the MetaSub remarkably improves the productivity of strain-induced devices in structural health monitoring (SHM) applications, internet of thing (IoT) networks, micro electromechanical (MEMS) systems, vibration energy harvesters, sensor and actuators, and hundreds applications that its performance is related to their deformation capability. In this paper, a piezoelectric type of energy harvester is selected to be studied numerically as the first application of the MetaSub. The finite element results predict the average power output gained by the MetaSub piezoelectric energy harvester to be up to 19.2 times more than power generated by an equivalent conventional piezoelectric energy harvester.

Wind energy has been seen as a most potential renewable energy. However, in shore environments, the wind turbine usually suffers constant impact of sand and strong wind speed, which causes the surface to deteriorate: cracks may appear. To reduce the wind turbine operations and maintenance (O and M) cost, assembling a self-powered surface condition monitoring system (SCMS) becomes the most important measures. In this paper, a meso-scale piezoelectric energy harvester (PEH) was fabricated, based on a tapered cantilever beam to scavenge the rotational energy to power SCMS. The advantages are to increase its output power density and its lifetime comparing to the traditional rectangular cantilever beam. A frequency up-conversion method was adopted to accommodate PEH to working under variety of rotational speed by using two opposing magnets. With different distances between two magnets, the output voltage and the daily output energy of the PEH were investigated under 5 rpm – 30 rpm rotational speed. The maximum output voltage is 2.7 V, 9.1 V and 13.6 V when the magnets spacing is 3 mm, 2 mm and 1 mm, respectively. For the magnet spacing of 1 mm, the daily output energy of the PEH was estimated to be 5.26 J under periodic magnetic plucking at 30 rpm, much higher than the 0.2 J of SCMS’s daily energy consumption, making this harvester an excellent solution for the abovementioned needs.

Since 1986, the above-ground dry cask for spent fuel rods was employed to the Surry Nuclear plant as a temporary solution for high-level waste storage until a more permanent solution could be found. During the service of the dry cask storage, when the crack occurs, it will grow progressively as time goes on. This greatly threatens the safety of the multilayer dry cask structures. Ultrasonic Lamb waves have been shown as an effective nondestructive evaluation (NDE) method due to their ability to propagate a long distance with less energy loss as well as their sensitivity to various defects on the surface or inside the structure. In this study, the research was focused on the laboratory investigation of the nondestructive inspection of the multilayer structures using a fully non-contact Lamb wave method. The non-contact system was constructed using an air-coupled transducer (ACT) for actuation and scanning laser Doppler vibrometer (SLDV) for sensing. The ACT provided a narrowband wave actuation, while SLDV provided high-quality wavefield signals for damage detection and evaluation. To systematically develop the method for dry cask structure inspection, crack inspection in a simple 1-mm aluminum plate was first conducted; then crack inspections in more complicated multilayer structures were further carried out. Besides, to evaluate the crack inspection, two imaging techniques were further developed using a full-wave energy method and a scattered wave energy method for crack detection. The cracks in both simple aluminum plate and multilayer structures were successfully inspected.

We are currently experiencing the next industrial revolution (industry 4.0). Production facilities and product parts communicate during manufacturing process. The properties of products can now be simulated before production using modeling which creates digital twins. But even under these advanced manufacturing conditions natural variability and “scatter” in material properties and occurrence of material defects cannot be completely ruled out. To provide advanced material characterization (or material state awareness) requires a new kind of cyber-based NDE where inspection processes are planned and optimized virtually using digital twins. The optimized inspection process has to be integrated into the cyber-controlled process and inspection results for individual parts have to be evaluated. Such NDE data is stored to and insights used to improve reliability and enable both forecasting and lifecycle management (prognostics). We call this NDE 4.0 where NDE data become a valuable resource. This contrasts with classical NDE approaches where the ability and experience of an inspector is required to perform and evaluate the NDE results, all in the context of a procedure where there has been a statistical POD assessment developed. Techniques developed in the past are now being viewed under a new light and are gaining in importance. The paper will present the IRMS, Inspection and Revision Management System, an approach that is supporting all necessary processing steps related to inspection and needed responses to better manage system life cycle at power plants, chemical plants, and in other industries based on a modular design beyond process limits. The use of IRMS creates a database of all inspections and component data, it supports inspection planning, and is a tool for prognostics and life-cycle management of components.

Energy storage technology and its efficient deployment will be increasingly needed to manage intermittent of renewable energy supply. Large scale storage can support a power grid, as with pumped hydroelectric, or small-scale battery systems can be used to support rooftop PV within a single building. This work considers a microgrid which supplies renewable energy (solar PV) to a single building with both electric (battery) and thermal (hot-water) energy storage. The goal is to explore the potential benefit of using artificial neural network with model productive control algorithm to predict the load demand and energy supply in order to lower the cost of storage system to assist in managing renewable power fluctuations, which is appropriate when significant thermal loads are present. The building considered here contains of apartments, and hourly electrical consumption and thermal demand are developed from historical meter data. Renewable energy from a PV array is dispatched to the load or is stored for later use, and the microgrid performance is measured by the renewable energy penetration, renewable curtailment, and system cost over time. Modeling results indicate that predict the load demand supply lower the cost of the energy. Also, increasing renewable energy penetration and decrease renewable energy curtailment.

Composites have been extensively used in aero structure and become the predominate components in the new airframes. Thus, rapid and effective inspection for composite structures is highly desired in aerospace engineering in order to shorten the certificate cycle for new structures or provide safety guarantee for existing ones. In this paper, a laser based remote Lamb wave inspection system is presented and implemented on composite plates for simulated damage detection. The system employs pulsed laser (PL) and scanning laser Doppler vibrometer (SLDV) for noncontact and remote Lamb wave actuation and wavefield sensing. A composite plate with simulated defect (surface bonded quartz rod) is inspected with the PL-SLDV laser system. Wave scattering are observed in the SLDV acquired wavefield and the damage is further evaluated with wavefield imaging and frequency wavenumber analysis. Potential application towards automatic PL Lamb wave excitation is also explored through employing an industry robotic arm towards rapid inspection.

Pressure sensors that can provide both high temporal and spatial resolutions are desired for the measurement of aerodynamic and acoustic events, ultrasonics, and underwater phenomena. Piezoelectric materials are attractive candidates for measuring dynamic pressure due to their high sensitivity, high signal-to-noise ratio, and potential for miniaturization. However, their inability to directly measure static pressure prevents their use in many applications. Due to their strong pyroelectric response, their use is also generally limited to conditions where the rate of temperature change is below the lower cutoff frequency of the measurement system. Polyvinylidene fluoride (PVDF) is a polymer with a high piezoelectric sensitivity which is readily available as a flexible, tough film. Under steady ow conditions, configuring PVDF as a cantilever unimorph provides a higher pressure sensitivity than alternatives such as compressive, doubly clamped, or diaphragm configurations. In this work, we demonstrate a differential aerodynamic pressure sensor based on a cantilever PVDF unimorph that has been optimized to maximize pressure sensitivity for a targeted deflection sensitivity. The sensor is characterized using a laboratory-scale wind tunnel for flows ranging from 0 to 12 ms^-1. Near-static measurements are enabled by a compensated charge amplifier with an extremely low cutoff frequency. The pyroelectric voltage generated from changes in the air ow temperature is compensated using a PVDF sensor in compressive mode. Within the tested pressure range of 0 to 80 Pa, the sensor exhibits a proportional response with a sensitivity of 0.97 mV Pa^-1.

While dealing with structures equipped with operating mechanical devices, keeping the machinery-induced vibrations below the acceptable limits is of enormous importance. The fundamental step to controlling undesirable vibrations in such structures is to localize the vibration source. The accuracy of locating the source of vibration using different methods, e.g., Time Difference of Arrival (TDOA) or Steered Response Power (SRP) method, depends on accurate estimation of the propagation speed. The propagation speed is a function of vibration frequency. The objective of this study is to investigate a nonlinear regression model to obtain the relationship between Wave Propagation Speed (WPS) and the vibration frequency on a concrete floor. The development of this relationship is based on a series of experiments on a concrete floor in a building using a shaker as a vibration exciter, and four accelerometers to record vertical vibration. First, the shaker generates sinusoid forces with a specific frequency and the accelerometers, configured collinearly, record acceleration measurements. Then, the WPS is estimated using cross-correlation to measure the time difference of arrival between pairs of accelerometers. This process is repeated for a range of frequencies resulting in a dataset that includes the vibration frequency as independent and the WPS as dependent variables. The relationship between speed and frequency is then optimally estimated using a nonlinear regression model.

In the process of bridge health monitoring, in order to study the relationship between strain and temperature at the important position of the structure, based on the field monitoring data of the Changqing Yellow River Bridge in Jinan, this paper analyzes it. According to the time series of temperature and strain data collected in the field stable operation stage, the basic characteristics of temperature and strain data are obtained through characteristic analysis. The wavelet analysis method is used to decompose the temperature and strain data in four layers to get different level of detail signals, including the strain caused by high-frequency random load such as vehicle load and noise, the strain caused by dead load and material creep, and the strain caused by daily temperature change. The relationship between temperature and strain is obtained by comparing the signal layer related to daily temperature change with the time series of temperature change. The results show that: 1. Temperature has a great influence on the strain of the structure, and temperature and strain show a positive correlation. 2. In different positions of the structure, the longitudinal distribution of temperature is more uniform. 3. All kinds of strain signals are obtained by wavelet decomposition. In a word, the data analysis method and research results used in this paper provide an important reference for bridge health monitoring in practical engineering.

In recent years, large-span metal roof is often exposed by wind. By analyzing and summarizing the reasons, this paper proposes an intelligent strengthening system for metal roof panel system. The structural health monitoring and distributed optical fiber sensing technology are organically applied to the metal roof reinforcement system. Based on the intelligent material reinforced fiber composite sensing material, which has both stress and sensor, and based on the distributed strain calculation, the high tensile capacity of the reinforced fiber composite sensor material is used to realize that the metal roof panel will not be lifted in the extreme wind days, At least achieve the effect of being lifted and not being blown away. This intelligent reinforcement system of metal roof based on optical fiber sensing technology is applied to the actual project of Xuzhou East Railway Station, hoping to provide reference for similar projects in the future.

There is an increasing interest in detecting heat loss through buildings using unmanned aerial vehicles (UAVs) and thermal sensors. The present study constitutes an attempt to develop a system that can detect heat loss in different inaccessible portions of building structures, such as roofs and high-rise facades. Traditionally, inspectors have conducted surveys to investigate insulation performance and detect heat loss through various portions of buildings. However, these kinds of surveys tend to be time-consuming, costly, and risky. To mitigate risks, a small, low-cost Adafruit thermal infrared sensor and a small, onboard Raspberry Pi microcontroller were mounted on a UAV to detect heat loss through buildings. The lightweight Raspberry Pi microcontroller and Adafruit thermal sensor were powered by additional batteries. A lightweight battery was selected based on the maximum payload and power demand of the microcontroller and thermal sensor. The Raspberry Pi was controlled remotely by a portable computer. The UAV flight plan was controlled remotely by FreeFlight Pro software. Several experimental tests were conducted in both indoor and outdoor environments. Both video and image data were obtained remotely from the thermal sensor and microcontroller. A standard FLIR thermal camera with a very high resolution was also used to ensure the accuracy of the results obtained from the UAV-based thermal sensor. All the images captured by the Adafruit thermal sensor were compared with the standard thermal camera images. The results showed that the presently developed system can detect heat loss through inaccessible locations in buildings with modifications only in sensor resolution.

Assessment of health state of large-scale infrastructure systems are crucial to ensure their operational safety. In this study, we propose the image-based conditional assessment of large-scale systems using deep learning approaches. The deep convolutional neural networks are optimally designed for satellite images to extract the sensitive features for assessment. The findings show that the machine learning methods exhibit great potential for infrastructure assessment, such as high bridges, and oil/gas pipeline assessment at both spatial and temporary scales over conventional methods.

In order to avoid the safety problems of the public building structure in the dense passenger flow environment, the structural health monitoring system is combined with the Building Information Modeling (BIM) method. This paper proposed a dynamic building information management system based on Structural Health Monitoring (SHM) information to ensure the security of its passenger flow information for a crowded exhibition hall. The continuous development of BIM (building information modeling) technology has greatly promoted the development of the construction industry. We can achieve real-time updating and visualization of sensor monitoring data and effectively manage different types of monitoring information as well as improve the controllability and safety of building structure monitoring by introducing BIM technology into structural health monitoring. A static building information model (BIM) was established to generate dynamic passenger flow information through video data learning, and provide a basis for structural health monitoring system for structural safety analysis and emergency response functions under dense passenger flow conditions. A dynamic building information management system suitable for structural health monitoring was built to display the dynamic network of traffic capacity in real time, so that the building information model is dynamic and predictable. The establishment for the combination mechanism of BIM and SHM system improved the data information circulation of BIM system. It could carry out dynamic health monitoring management and early warning control of building structures, realize information sharing in the process of health monitoring, and effectively improve the safety and operational efficiency of public building structures.

This paper develops an auxetic cantilever beam energy harvester (ACBEH) to enhance the harvesting power from ambient vibration sources. A finite element analysis was performed to verify the power increase mechanism of the ACBEH. The simulation model of the ACBEH comprises of three main components: support, tip mass, and cantilever beam with a re-entrant hexagonal auxetic structure in which a piezoelectric element bonded to top of the auxetic region by using a thin elastic layer of epoxy. The performance of the ACBEH was computationally investigated and compared with an equivalent conventional energy harvester with a plain cantilever beam where they are attached to a bridge stay cable. The simulation result shows that the ACBEH excited by a harmonic acceleration of 1 m/s² at 3 Hz is able to produce electric power of 427.22 μW, which is 2.51 times that of the power produced by the equivalent plain cantilever beam energy harvester (170.17 μW). This paper opens up a great potential of using auxetic cantilever beam applications for different energy harvesting systems in Metamaterials, Acoustics, Civil, Electrical, Aerospace, Biomedical, and Mechanical Engineering.

Future smart and autonomous vehicles offer opportunities to improve the comfort of drivers and passengers during their journey. Entertainment activities or video conferencing within vehicles require high quality projection surfaces. Vehicle interior surfaces are expected to undergo a revolutionary change to meet the needs of future consumers. Use of technologies such as a 5G network, machine learning and cloud communication create the opportunity to personalize the vehicle experience. Driving information, advertisements, and information on-demand can be displayed as needed on projection surfaces for improved communication and convenience. Interactive surfaces will improve the customer experience by allowing the vehicle occupant to easily control and give commands within the vehicle. Vehicles will also sense the customer and automatically react to specific needs. In this paper, we will discuss technology trends and our evaluation of opportunities and limitations.

Roads are important parts of infrastructure. The detection of road condition plays an important role for the traffic safety. Vehicles, weather and other factors will cause different types of damage to the road surface. To avoid this happening, the commonly used method is manual inventory at present, which is time-consuming, laborious and easily leads to omissions. In order to overcome these shortcomings, this paper presents a method of road damage detection based on machine vision, which is more efficient and relatively cheap. To realize the method, the author used the Raspberry Pi, acceleration sensor, GPS module, Neural Compute Stick and camera to complete the design of intelligent inspection terminal. Then the author investigated the common types of road damage, including long strip cracks, reticulation cracks, potholes, and rutting. After that, an SSD-mobilenet architecture was modified and a database including a large number of images for different types of damage was built. The SSD-mobilenet was trained and validated with the built database. Transplanting the SSD-mobilenet to the intelligent inspection terminal, which could realize the road damage detection based on machine vision. The result shows 80.87% average precision (AP) ratings for different types of damage and proves the proposed method is effective.

When a large-scale seismic data acquisition and recording system is applied for long-term continuous data acquisition, it is often affected by factors such as temperature, excitation level and acceleration changes perceived by the crystal unit. Since there is no accumulative error for GPS time and the short-term stability of GPS local clock is outstanding, this paper designs a scheme that uses GPS clock as standard clock to time FCU(Field Control Unit), then uses GPS highprecision second pulse, and uses least square method and bisection method to calibrate local clock frequency. The test results show that the clock synchronization error of this scheme is less than 800ns, which has the advantages of low cost and good reliability, and can meet the needs of clock synchronization for long-time continuous data acquisition of largescale seismic acquisition and recording system.

Fiber Bragg grating (FBG) sensors are obviously attractive for many prominent advantages, such as strong antielectromagnetic interference ability, long transmission distance, and fast transmission speed. Due to these advantages, FBG sensors can be used for the health monitoring of high-voltage transmission lines. The FBG sensor can not only ensure the security of the transmission lines, but can realize real-time monitoring, which can effectively prevent serious accidents caused by rust breakage of overhead steel strand. In this paper, a new type of corrosion sensor based on FBG was developed to monitor and evaluate the degree of corrosion damage of the steel strand. To verify the performance of the FBG corrosion sensors, an electrochemical corrosion accelerated experiment was conducted on an actual steel strand. The experimental results show that the FBG corrosion sensors have a large measurement range of steel strand corrosion rate, and can monitor steel strand corrosion expansion damage in early stage effectively. The FBG corrosion sensor has practical application value in the field of electricity transmission engineering.

This study aims in examining the fracture behavior of recycled mortar specimens using the acoustic emission technique. To produce the recycled mortar beams, a portion of fine recycled concrete aggregates has been used, and the specimens were tested in three-point bending. This work led to a comparison between the fracture behavior of recycled mortar specimens with steel fiber-reinforced and baseline mortars fabricated with 100% natural sand. The results indicate that the use of recycled aggregates in mortars can be successfully characterized by acoustic emission parameters. This approach offers a reliable evaluation of the fracture mechanism making acoustic emission a valuable nondestructive evaluation tool in the growing sector of recycled building materials.

The present paper deals with the acoustic emission (AE) monitoring of fracture behavior of repaired marble specimens. Different types of specimens were ultrasonically interrogated. Subsequently, damage was induced to these specimens by three-point bending. The damaged specimens were repaired using a suitable epoxy agent; then they were mechanically loaded again. Apart from the well-known correlation of pulse velocity to strength for building materials, which also holds for the materials used in this study, AE provides a unique insight in the fracture behavior of the specimens. A statistical analysis of the experimental data has been performed to investigate the correlation between AE parameters and the strength of the specimens. This work discusses the passive monitoring of fracture in repaired marble specimens and shows that AE parameters, well-known to successfully characterize cementitious materials, also provide satisfactory results in characterizing monolithic materials such as marble. It is concluded that AE monitoring during a proof loading can provide good insight information of the materials and characterize their restoration.

Different measurement methodologies have been used in bridge monitoring due to seismic, environmental and operating loading. Moreover, bridge monitoring systems have influence in the smooth operation of the traffic load in big cities and thus it’s crucial to encourage monitoring techniques that are flexibly adaptable to various construction building models. Even though a lot of research has been performed in bridge health monitoring in order to identify damages or deterioration of the structural elements there is still a need for a method that could combine multisensor techniques in big data processing for multisource loading. The behavior of the cable-stay bridge model is being monitored via acoustic emission and 2D laser Doppler vibrometry systems. In each case, both static and dynamic loading conditions have been applied. The goal is to correlate the results of these nondestructive evaluation techniques during static and dynamic response of different support situations. The purpose of this work is to improve bridge design and enable the detection of distributed failures during a multifactor loading system.

The demolition waste that is produced by the construction industry is one of the highest generators of solid waste worldwide. Moreover, the construction industry consumes massive amounts of all extracted natural resources. It is thus crucial to encourage more sustainable, environmental and economical construction practices. Nowadays, the proper modification of the recycled aggregates is of high demand because they mitigate the main disadvantages of recycled aggregates, like the increased porosity and water absorption. Although a lot of research has been performed in the modification of coarse recycled aggregates, the modification of fine recycled aggregates has not been adequately investigated. In this study, the outer surface of fine recycled concrete aggregates has been modified by coating them with three different types of modification cement paste, spreadable, elastomeric, water-soluble sealant, and a mixed variety of both modifications. The coated samples were nondestructively examined through the thickness (longitudinal mode), and the ultrasound velocity measurements were processed by a new developing method based on MATLAB that can provide automatically enhanced and high-quality data. The goal is to compare the influence of the cement paste coating of the fine recycled concrete aggregates utilizing ultrasound velocity measurements during the hardening of mortar. Results showed that the coating modification of fine recycled concrete aggregates affects the water absorption, as well as the elastic properties of the mortar. This led to a better understanding of the mechanism of hydration in recycled aggregates mortars, as well as in recycled aggregates concrete.

Smart materials are an effective method for increasing safety and reliability across a range of applications. Intrinsic selfsensing mortar is one such material that could greatly improve cementitious infrastructure through the use of real time sensing and monitoring capabilities. This research aims to investigate the self-sensing behavior of mortar, when varying the volume of a stainless-steel functional filler. The results demonstrated a direct correlation between the applied stress and measured electrical resistivity of a sample. Infrared thermography has been also applied for the monitoring of the fracture behavior under monotonic flexure load.