Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Feasibility Study for Detection and Quantification of Corrosion in Bridge Barrier Rails, RB11-012, 2013

"Technical challenges exist with infrastructure that can be addressed by nondestructive evaluation (NDE) methods, such as detecting corrosion damage to reinforcing steel that anchor concrete bridge railings to bridge road decks. Moisture and chloride ions reach the anchors along the cold joint between the rails and deck, causing corrosion that weakens the anchors and ultimately the barriers. The Center for Nondestructive Evaluation at Iowa State University has experience in development of measurement techniques and new sensors using a variety of interrogating energies. This research evaluated feasibility of three technologies — x-ray radiation, ground-penetrating radar (GPR), and magnetic flux leakage (MFL) — for detection and quantification of corrosion of embedded reinforcing steel. Controlled samples containing pristine reinforcing steel with and without epoxy and reinforcing steel with 25 percent and 50 percent section reduction were embedded in concrete at 2.5 in. deep for laboratory evaluation. Two of the techniques, GPR and MFL, were used in a limited field test on the Iowa Highway 210 Bridge over Interstate 35 in Story County. The methods provide useful and complementary information. GPR provides a rapid approach to identify reinforcing steel that has anomalous responses. MFL provides similar detection responses but could be optimized to provide more quantitative correlation to actual condition. Full implementation could use either GPR or MFL methods to identify areas of concern, followed by radiography to give a visual image of the actual condition, providing the final guidance for maintenance actions." The full 103 page report and the 2 page Tech Transfer Summary are included in this link.

Damage analysis of bridge structures using vibrational techniques

Much research is currently centred on the detection of damage in structures using vibrational data. The work presented here examined several areas of interest in support of a practical technique for identifying and locating damage within bridge structures using apparent changes in their vibrational response to known excitation. The proposed goals of such a technique included the need for the measurement system to be operated on site by a minimum number of staff and that the procedure should be as non-invasive to the bridge traffic-flow as possible. Initially the research investigated changes in the vibrational bending characteristics of two series of large-scale model bridge-beams in the laboratory and these included ordinary-reinforced and post-tensioned, prestressed designs. Each beam was progressively damaged at predetermined positions and its vibrational response to impact excitation was analysed. For the load-regime utilised the results suggested that the infuced damage manifested itself as a function of the span of a beam rather than a localised area. A power-law relating apparent damage with the applied loading and prestress levels was then proposed, together with a qualitative vibrational measure of structural damage. In parallel with the laboratory experiments a series of tests were undertaken at the sites of a number of highway bridges. The bridges selected had differing types of construction and geometric design including composite-concrete, concrete slab-and-beam, concrete-slab with supporting steel-troughing constructions together with regular-rectangular, skewed and heavily-skewed geometries. Initial investigations were made of the feasibility and reliability of various methods of structure excitation including traffic and impulse methods. It was found that localised impact using a sledge-hammer was ideal for the purposes of this work and that a cartridge `bolt-gun' could be used in some specific cases.

Loading and damage modelling for an integrated SHM system of a prestressed concrete railway bridge in Naples

The thesis explores recent technology developments in the field of structural health monitoring and its application to railway bridge projects. It focuses on two main topics. First, service loads and effect of environmental actions are modelled. In particular, the train moving load and its interaction with rail track is considered with different degrees of detail. Hence, results are compared with real-time experimental measurements. Secondly, the work concerns the identification, definition and modelling process of damages for a prestressed concrete railway bridge, and their implementation inside FEM models. Along with a critical interpretation of the in-field measurements, this approach results in the development of undamaged and damaged databases for the AI-aided detection of anomalies and the definition of threshold levels to prompt automatic alert interventions. In conclusion, an innovative solution for the development of the railway weight-in-motion system is proposed.

Single side damage simulations and detection in beam-like structures

Beam-like structures are the most common components in real engineering, while single side damage is often encountered. In this study, a numerical analysis of single side damage in a free-free beam is analysed with three different finite element models; namely solid, shell and beam models for demonstrating their performance in simulating real structures. Similar to experiment, damage is introduced into one side of the beam, and natural frequencies are extracted from the simulations and compared with experimental and analytical results. Mode shapes are also analysed with modal assurance criterion. The results from simulations reveal a good performance of the three models in extracting natural frequencies, and solid model performs better than shell while shell model performs better than beam model under intact state. For damaged states, the natural frequencies captured from solid model show more sensitivity to damage severity than shell model and shell model performs similar to the beam model in distinguishing damage. The main contribution of this paper is to perform a comparison between three finite element models and experimental data as well as analytical solutions. The finite element results show a relatively well performance.

An Adaptive Field Detection Method For Bridge Scour Monitoring Using Motion-Sensing Radio Transponders (RFIDs), TR-617, 2014

A comprehensive field detection method is proposed that is aimed at developing advanced capability for reliable monitoring, inspection and life estimation of bridge infrastructure. The goal is to utilize Motion-Sensing Radio Transponders (RFIDS) on fully adaptive bridge monitoring to minimize the problems inherent in human inspections of bridges. We developed a novel integrated condition-based maintenance (CBM) framework integrating transformative research in RFID sensors and sensing architecture, for in-situ scour monitoring, state-of-the-art computationally efficient multiscale modeling for scour assessment.

Detection of Steel Corrosion in Bridge Decks and Reinforced Concrete Pavement, HR-156, 1977

The Iowa Department of Transportation initiated this research to evaluate the reliability, benefit and application of the corrosion detection device. Through field testing prior to repair projects and inspection at the time of repair, the device was shown to be reliable. With the reliability established, twelve additional devices were purchased so that this evaluation procedure could be used routinely on all repair projects. The corrosion detection device was established as a means for determining concrete removal for repair. Removal of the concrete down to the top reinforcing steel is required for all areas exhibiting electrical potentials greater than 0.45 Volt. It was determined that the corrosion detection device was not applicable to membrane testing. The corrosion detection device has been used to evaluate corrosion of reinforcing steel in continuously reinforced concrete pavement.

Detection of beetle damage in forests by X-ray CT image processing

Some beetle species can have devastating economic impacts on forest and nursery industries. A recent example is Anophophora glabripennis, a species of beetle known in the United States as the ''Asian Longhorrned beetle'', which has damaged many American forests, and is a threat which can unintentionally reach south American countries, including Brazil. This work presents a new method based on X-ray computerized tomography (CT) and image processing for beetle injury detection in forests. Its results show a set of images with correct identification of the location of beetles in living trees as well as damage evaluation with time.

Chromosome damage in underground coal miners: detection by conventional cytogenetic techniques and by submitting lymphocytes of unexposed individuals to plasma from at-risk groups

Chromosome abnormalities and the mitotic index in lymphocyte cultures and micronuclei in buccal mucosa cells were investigated in a sample of underground mineral coal miners from Southern Brazil. A decreased mitotic index, an excess of micronuclei and a higher frequency of chromosome abnormalities (fragments, polyploidy and overall chromosome alterations) were observed in the miners when compared to age-paired normal controls from the same area. An alternative assay for clastogenesis in occupational exposition was tested by submitting lymphocytes from non-exposed individuals to a pool of plasmas from the exposed population. This assay proved to be very convenient, as the lymphocytes obtained from the same individuals can be used as target as well as control cells. Also, it yielded a larger number of metaphases and of successful cultures than with common lymphocyte cultures from miners. A significantly higher frequency of chromatid gaps, fragments and overall alterations were observed when lymphocytes from control subjects were exposed to miner plasma pools. Control plasma pools did not significantly induce any type of chromosome alterations in the cultures of normal subjects, thus indicating that the results are not due to the effect of the addition of plasma pools per se.

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Immunochemical detection of UV-induced DNA damage and repair

The application of an antiserum to ultraviolet radiation (UVR)-damaged DNA is presented. A novel experimental system was employed to ascertain the limits of detection for this antiserum. Using a DNA standard containing a known amount of dimer, the limits of detection were found to be 0.9 fmol of dimer. This was compared to a limit of 20-50 fmol dimer using gas chromatography-mass spectrometry (GC-MS). Induction of thymine dimers in DNA following UVR exposure, as assessed using this antiserum in an enzyme-linked immunosorbent assay (ELISA), was compared with GC-MS measurements. The ELISA method successfully demonstrated the induction of lesions in DNA irradiated either with UVC or UVB, although despite high sensitivity, no discernible binding was seen to UVA-irradiated DNA. The antiserum was also shown to be applicable to immunocytochemistry, localising damage in the nuclei of UVR exposed keratinocytes in culture. The ability of the antiserum to detect DNA damage in skin biopsies of individuals exposed to sub-erythemal doses of UVR was also demonstrated. Moreover, the subsequent removal of this damage, as evidenced by a reduction in antiserum staining, was noted in sections of biopsies taken in the hours following irradiation. © 2003 Elsevier B.V. All rights reserved.

Immunochemical detection of glyoxal DNA damage

The relevance of reactive oxygen species (ROS) in the pathogenesis of inflammatory diseases is widely documented. Immunochemical detection of ROS DNA adducts has been developed, however, recognition of glyoxal-DNA adducts has not previously been described. We have generated a polyclonal antibody that has shown increased antibody binding to ROS-modified DNA in comparison to native DNA. In addition, dose-dependent antibody binding to DNA modified with ascorbate alone was shown, with significant inhibition by desferrioxamine, catalase, and ethanol. Minimal inhibition was observed with uric acid, 1,10-phenanthroline and DMSO. However, antibody binding in the presence of EDTA increased 3500-fold. The involvement of hydrogen peroxide and hydroxyl radical in ascorbate-mediated DNA damage is consistent with ascorbate acting as a reducing agent for DNA-bound metal ions. Glyoxal is known to be formed during oxidation of ascorbate. Glyoxylated DNA, that previously had been proposed as a marker of oxidative damage, was recognised in a dose dependent manner using the antibody. We describe the potential use of our anti-ROS DNA antibody, that detects predominantly Fenton-type mediated damage to DNA and report on its specificity for the recognition of glyoxal-DNA adducts.