Nondestructive Evaluation Data Research Articles

FTnet: An integrated network for fusing multi-modal NDE data of lightning damage in aircraft composite materials

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage.

Information reuse of nondestructive evaluation (NDE) data sets

Abstract. To achieve added value from data spaces and data sets in general, an essential condition is to ensure the high quality of the stored information and its continuous availability. Nondestructive evaluation (NDE) processes represent an information source with potential for reuse. These provide essential information for the evaluation and characterization of materials and components. This information, along with others such as process parameters, is a valuable resource for data-driven added value, e.g., for process optimization or as training data for artificial intelligence (AI) applications. However, this use requires the continuous availability of NDE data sets as well as their structuring and readability. This paper describes the steps necessary to realize an NDE data cycle from the generation of information to the reuse of data.

Evaluating Diffusion Models for the Automation of Ultrasonic Nondestructive Evaluation Data Analysis

We develop decision support and automation for the task of ultrasonic non-destructive evaluation data analysis. First, we develop a probabilistic model for the task and then implement the model as a series of neural networks based on Conditional Score-Based Diffusion and Denoising Diffusion Probabilistic Model architectures. We use the neural networks to generate estimates for peak amplitude response time of flight and perform a series of tests probing their behavior, capacity, and characteristics in terms of the probabilistic model. We train the neural networks on a series of datasets constructed from ultrasonic non-destructive evaluation data acquired during an inspection at a nuclear power generation facility. We modulate the partition classifying nominal and anomalous data in the dataset and observe that the probabilistic model predicts trends in neural network model performance, thereby demonstrating a principled basis for explainability. We improve on previous related work as our methods are self-supervised and require no data annotation or pre-processing, and we train on a per-dataset basis, meaning we do not rely on out-of-distribution generalization. The capacity of the probabilistic model to predict trends in neural network performance, as well as the quality of the estimates sampled from the neural networks, support the development of a technical justification for usage of the method in safety-critical contexts such as nuclear applications. The method may provide a basis or template for extension into similar non-destructive evaluation tasks in other industrial contexts.

A study of machine learning object detection performance for phased array ultrasonic testing of carbon fibre reinforced plastics

The growing adoption of Carbon Fibre Reinforced Plastics (CFRPs) in the aerospace industry has resulted in a significant reliance on Non-Destructive Evaluation (NDE) to ensure the quality and integrity of these materials. The interpretation of large amounts of data acquired from automated robotic ultrasonic scanning by expert operators is often time consuming, tedious, and prone to human error creating a bottleneck in the manufacturing process. However, with ever growing trend of computing power and digitally stored NDE data, intelligent Machine Learning (ML) algorithms have been gaining more traction than before for NDE data analysis. In this study, the performance of ML object detection models, statistical methods for defect detection, and traditional amplitude thresholding approaches for defect detection in CFRPs were compared. A novel augmentation technique was used to enhance synthetically generated datasets used for ML model training. All approaches were tested on real data obtained from an experimental setup mimicking industrial conditions, with ML models showing improvement over amplitude thresholding and statistical thresholding techniques. The advantages and limitations of all methods are reported and discussed.

Dynamic Defect Detection in Fast, Robust NDE Methods by Transfer Learning Based Optimally Binned Hypothesis Tests

ABSTRACT The structural integrity of safety-critical infrastructures diminishes over time, necessitating periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) readings for defect detection can be both time-consuming and costly. In practical applications, NDE data can be highly noisy due to factors like liftoff/stand-off distances, probe drift, scanning speed, and variations in data acquisition rates. Given the broad spectrum of potential uncertainties that can contribute to noise accumulation, characterizing the noise in such NDE data presents significant difficulties. As a result, the extraction of defect signals through deconvolution becomes challenging. This article introduces an NDE-based dynamic defect tracking framework designed for robust conditions. It incorporates regular periodic monitoring of the material under test (MUT) using a cost-effective, easy-to-implement magnetic flux leakage (MFL) probe. The framework proposes a dynamic setup that includes occasional, highly accurate, expensive MFL scans among regular, low-cost, noisy MFL scans. The objective is to detect and precisely track defect formation in metallic pipes over time, thereby enabling the efficient alarming and localization of defects before they reach detrimental sizes. A key characteristic of the proposed dynamic monitoring method is its noise type agnosticism in the low-cost MFL scans. It remains effective even when the underlying noise-producing uncertainties fluctuate significantly over time, a feature achieved through the use of transfer learning. The method transfers accurate information regarding the locations and sizes of existing corrosions from the detailed MFL scan to the subsequent noisy MFL inspections. With this supportive information from defective scan-points, it estimates the degradation of the signal-to-noise ratio (SNR) in the noisy MFL scans. The framework suggests binned hypothesis tests in noisy MFL scans, setting the level of aggregation based on the SNR estimate in the scan data. This proposed binned hypothesis test optimizes defect coverage and maintains control over the false discovery rate (FDR). Experimental MFL data, gathered under a wide range of uncertainties and defect types, illustrates the highly heterogeneous nature of noise distributions in low-cost MFL scans and the significant variance in SNR deteriorations. The necessity of using binned hypothesis tests in these low-cost MFL data is highlighted by the poor performance of pointwise multiple hypothesis tests. Finally, the high effectiveness of the proposed transfer learning-based binned hypothesis testing method is demonstrated. Although the method was applied to MFL data in this instance, it is also suitable for other NDE applications. The applicability of the algorithms has also been confirmed through validation on experimentally generated Eddy Current (EC) data.

Deploying Machine Learning for Radiography of Aerospace Welds

Artificial intelligence is providing new possibilities for analysis in the field of industrial radiography. As capabilities evolve, there is the need for knowledge concerning how to deploy these technologies in practice and benefit from the new automatically generated information. In this study, automatic defect recognition based on machine learning was deployed as an aid in industrial radiography of laser welds in an aerospace component, and utilized to produce statistics for improved quality control. A multi-model approach with an added weld segmentation step improved the inference speed and decreased false calls to improve field use. A user interface with visualization options was developed to display the evaluation results. A dataset of 451 radiographs was automatically analysed, yielding 10037 indications with size and location information, providing capability for statistical analysis beyond what is practical to carry out with manual annotation. The distribution of indications was modeled as a product of the probability of detection and an exponentially decreasing underlying flaw distribution, opening the possibility for model reliability assessment and predictive capabilities on weld defects. An analysis of the indications demonstrated the capability to automatically detect both large-scale trends and individual components and welds that were more at risk of failing the inspection. This serves as a step towards smarter utilization of non-destructive evaluation data in manufacturing.

Enhanced defect detection in NDE using registration aided heterogeneous data fusion

The degradation of systems in service, including pipelines, over time highlights the critical need for reliable and accurate defect detection to ensure safe operations. However, single modality-based Nondestructive Evaluation (NDE) data used in practical applications often suffers from noise contamination and errors caused by various factors like lift-off/standoff distances, probe drift, scanning speed, variation in data acquisition rates, and poor sensor sensitivity. The presence of agnostic noise types poses a challenge in extracting defect signals. To address these challenges, this paper presents an automated NDE theory-based data fusion framework aimed at enhancing the detection of surface and near-surface defects in magnetizable and conductive specimens. The Magnetic Flux Leakage (MFL) and Eddy Current (EC) based NDE sensing methods demonstrate the highly heterogeneous nature of noise distributions. Given the heterogeneity of the inspection methods, a screening rule is proposed to determine the conditions under which fusion would be beneficial. An important aspect of the proposed fusion method is registration, which ensures accurate alignment of multi-sensor image data. Two registration methods are proposed in this study as performing blind fusion without registration leads to erroneous results. The first registration method is translational, whereas the second method is registration based on linear optimal transport (OT) which proves to be effective in the boundary conditions. Finally, the registered source images from the EC and MFL modalities are fused using pixel-based fusion algorithms, including transform domain and spatial domain-based methods. Qualitative and quantitative assessments demonstrate that the registered fusion results exhibit higher accuracy and reliability compared to unregistered fused results and source images. Although the fusion method is applied to MFL and EC data in this paper, it is also suitable for other NDE modalities.

Multi-resource fusion of nondestructive evaluation data for bridge deck assessment using discrete wavelet transform and Dempster-Shafer theory

Nondestructive evaluation (NDE) methods are widely used to detect defects in bridge decks. These methods evaluate the bridge deck condition from different aspects. Data fusion is a viable approach to efficiently process and combine such heterogeneous NDE data for making more informed decisions. In order to fuse multi-resource NDE data, it is crucial to avoid the counter-intuitive results due to potential conflict between the measurements. In this study, discrete wavelet transforms (DWT) and improved Dempster-Shafer (D-S) evidence combination theory are proposed to develop a multi-resource NDE data fusion framework. A series of NDE data periodically collected through a full-scale bridge accelerated testing program are used to create the proposed framework. The deployed NDE methods include half-cell potential, ground penetrating radar, electrical resistance, and ultrasonic waves. The results from the data fusion analysis are compared with those derived using individual NDE, visual inspection, and advanced vision-based methods. By leveraging the access to the unique data sets collected from the Bridge Evaluation and Accelerated Structural Testing (BEAST) facility, the feasibility of the proposed method has been evaluated for the entire bridge lifetime under a controlled environment. The efficacy of the fused NDE data in detecting various bridge defects is further discussed via correlating the information captured during the accelerated bridge testing with a representative bridge in the state of Pennsylvania.

A Generalized Classification Framework with Simultaneous Feature Weighting and Selection Using Antlion Optimization Algorithm

ABSTRACT The use of machine-learning based algorithms on a large-scale nondestructive evaluation (NDE) data considerably advances the NDE techniques toward complete industrial automation. In this article, simultaneous feature selection and feature weighting are carried out on the magnetic Barkhausen emission (MBE) dataset to demonstrate the significance of optimization in NDE data. Antlion optimization is employed as a searching method to determine the optimum feature set that will maximize the classification performance. The proposed framework is validated for different magnetization frequencies separately and found to be frequency independent. The framework resulted in the selection of four significant features extracted from the MBE response thereby reducing the computational effort and improving the accuracy to 98.4% for AdaBoost classifier. The developed machine learning methodology is a potential strategy for processing industrial sensory data since material testing, property prediction, and categorization are frequent tasks in manufacturing and production engineering industries. Further, this research demonstrated the necessity of embedded intelligence in automation of NDE toward Industrial Revolution 4.0.

Reliability and failure modelling of microelectronic packages based on ultrasonic nondestructive evaluation data

Reliability testing and failure modelling is crucial and very challenging for modern electronics, especially safety critical electronics-based systems working under harsh environmental conditions. In this paper, an approach based on ultrasonic Non-Destructive Evaluation (NDE) is proposed to establish a failure model for solder joints which is urgently needed for prognostic and health management of electronics. Printed Circuit Boards (PCB) containing flip-chip and Area Array packages were designed and aged using Accelerated Thermal Cycling (ATC) testing. During ATC testing solder joint degradation was regularly monitored by taking out the test boards from the temperature chamber at four thermal cycle intervals for ultrasonic micro-imaging. Data pre-processing techniques including dynamic range-based intensity normalization, gain compensation, and calibration for random transducer defocusing errors were developed to enhance the data consistency, integrity and accuracy. Furthermore, solder joint image labelling and segmentation methods were developed by exploiting the geometrical features of the solder joints to extract individual solder joints and Region of Interests (ROIs) from the sequential ultrasound images collected throughout the testing. The mean intensity of the ROIs was used as the precursor to degradation to build the solder joint failure model, from which cycles to failure for individual solder joints are finally derived for life prediction.

Classification of Impact Echo Signals Using Explainable Deep Learning and Transfer Learning Approaches

Impact echo (IE) is one of the most frequently used nondestructive evaluation (NDE) techniques for detecting subsurface defects such as delamination, honeycombing, and voids in concrete structures. In the conventional analysis of IE data, the time-domain signal is transformed into the frequency domain and the frequency content is used to estimate the presence and nature of the defect. Machine learning (ML) has been recently applied to the IE signal classification problem. However, because of the scarcity of labeled IE datasets, most existing work relies on relatively small training and test datasets without addressing the generalizability and transferability of the developed models. In this paper, we compare two approaches for automatic classification of IE signals: clustering based on expert-crafted features and deep learning (DL) from automatically extracted features. Next, we use the knowledge gained from a DL model trained on concrete specimens with available ground truth to make predictions about defects in a different specimen with completely different construction and characteristics (transfer learning). Finally, we examine our DL model to gain insights into the model working (explainability) and highlight the attributions that are significant in classifying a particular IE signal. Our findings demonstrate the utility of ML and DL for IE signal classification, but also highlight the need for high-quality labeled datasets for advancing ML and DL in NDE data analysis.

Pulsed Thermography Dataset for Training Deep Learning Models

Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area of research. This has motivated efforts to apply deep learning methods to non-destructive evaluation data processing, including pulsed thermography. Despite this trend, there has been a lack of public pulsed thermography datasets available for the evaluation of various spatial-temporal deep learning models for segmentation tasks. This paper aims to address this gap by presenting the PVC-Infrared dataset for deep learning. In addition, we evaluated the performance of popular deep-learning-based instance segmentation models on this dataset. Furthermore, we examined the effect of the number of frames and data transformations on the performance of these models. The results of this study suggest that appropriate preprocessing techniques can significantly reduce the size of the data while maintaining the performance of deep learning models, thereby speeding up the data processing process. This highlights the potential for using deep learning methods to make non-destructive evaluation data analysis more efficient and accessible to a wider range of practitioners.

Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey

AbstractApplication of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. From the published literature, it is evident that lot of research efforts has been done in identifying the surface defects such as cracks, potholes, spalling and so forth using supervised learning methods such as deep learning. However, the health of a reinforced concrete bridge deck is jeopardized substantially due to presence of subsurface defects. Subsurface defects in bridge decks are genearlly detected using non‐destructive evaluation (NDE) methods. Interpertation of NDE data for autonomous deck evaluation requires development of DL models; however, The task of defect detection DL has not received the proper attention for subsurface defect detection in the past. The goal of this paper is to provide a review of existing DL models for analysis of NDE data of bridge decks. The authors reviewed prominent NDE techniques for subsurface defect detection of bridge decks and explored the various DL models proposed to identify these defects. First a brief overview of the working principle of NDE techniques and DL architectures is provided, and then the information about proposed DL models and their efficacy is highlighted. Based on the existing knowledge gaps, various challenges and future prospects associated with application of DL in bridge subsurface inspection are discussed.

SDNET2021: Annotated NDE Dataset for Subsurface Structural Defects Detection in Concrete Bridge Decks

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset.

Visually Encoded Contact Inspection System for EMATs

Contact inspections are commonly performed in industry to check for defects and degradation, such as corrosion or cracks. Non-destructive evaluation (NDE) probes are being deployed with increasing frequency using autonomous robots, especially in harsh environments or in areas where access is restricted and difficult. Together with the NDE measurement, it is important to capture the 3D position of the probe so that the location where the data originated is known. This allows for the generation of 3D maps of the inspection, which ensure full scan coverage, and can be used for inspection reports and to generate digital twins of the structure for asset management. In this paper, a full inspection system integrating a robot mountable stereo camera system together with an electromagnetic acoustic transducer (EMAT) probe and a wireless NDE data acquisition system is presented. The system is capable of capturing and merging 3D positional data of the probe as it is scanned and NDE data. The system design in terms of hardware and software is described in this paper. A set of tests to evaluate its performance on relevant structural components are also presented, and the results are reported and discussed.

On ultrasound propagation in composite laminates: advances in numerical simulation

The growing use of composite materials for aerospace applications has resulted in the need for quantitative methods to analyze composite components. Ultrasonic guided waves constitute the physical approach for nondestructive evaluation (NDE) and structural health monitoring (SHM) of solid composite materials, such as carbon fiber reinforced polymer (CFRP) laminates. Ultrasound based NDE methods are commonly used in the aerospace field, but ultrasonic wave behavior can be complicated by the presence of material anisotropy, complex geometries (e.g., highly curved parts, stiffeners, and joints) and complex geometry defects. Common defects occurring in aerospace composites include delaminations, porosity, and microcracking. Computational models of ultrasonic wave propagation in CFRP composites can be extremely valuable in designing practical NDE and SHM hardware, software, and methodologies that accomplish the desired accuracy, reliability, efficiency, and coverage. Physics based simulation tools that model ultrasonic wave propagation can aid in the development of optimized inspection methods and in the interpretation of NDE data. This paper presents a review of numerical methodologies for ultrasound and guided wave simulation in fiber reinforced composite laminates summarizing the relevant works to date, different methods, and their respective applications.

A Novel Decision Support System for Long-Term Management of Bridge Networks

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.

Composite Material Failure Model Updating Approach Leveraging Nondestructive Evaluation Data

Abstract A novel failure model updating methodology is presented in this paper for composite materials. The innovation in the approach presented is found in both the experimental and computational methods used. Specifically, a dominant bottleneck in data-driven failure model development relates to the types of data inputs that could be used for model calibration or updating. To address this issue, nondestructive evaluation data obtained while performing mechanical testing at the laboratory scale are used in this paper to form a damage metric based on a series of processing steps that leverage raw sensing inputs and provide progressive failure curves that are then used to calibrate the damage initiation point computed by full-field three-dimensional finite element simulations of fiber-reinforced composite material that take into account both intra- and interlayer damage. Such curves defined based on nondestructive evaluation data are found to effectively monitor the progressive failure process, and therefore, they could be used as a way to form modeling inputs at different length scales.

Fusion and Visualization of Bridge Deck Nondestructive Evaluation Data via Machine Learning

To maintain infrastructure safety and integrity, nondestructive evaluation (NDE) technologies are often used for detection of subsurface defects and for holistic condition assessment of structures. While the rapid advances in data collection and the diversity of available sensing technologies provide new opportunities, the ability to efficiently process data and combine heterogeneous data sources to make robust decisions remains a challenge. Heterogeneous NDE measurements often conflict with one another and methods to visualize integrated results are often developed ad hoc. In this work, we present a framework to support fusion of multiple NDE techniques in order to improve both detection and quantification accuracy while also improving the visualization of NDE results. For data sources with waveform representations, the discrete wavelet transform (DWT) is used to extract salient features and facilitate fusion with scalar-valued NDE measurements. The description of a signal in terms of its salient features using a wavelet transform allows for capturing the significance of the original data, while suppressing measurement noise. The complete set of measurements is then fused using nonparametric machine learning so as to relax the need for Bayesian assumptions regarding statistical distributions. A novel visualization schema based on classifier confidence intervals is then employed to support holistic visualization and decision making. To validate the capabilities of the proposed methodology, an experimental prototype system was created and tested from NDE measurements of laboratory-scale bridge decks at Turner-Fairbank highway research center (TFHRC). The laboratory decks exhibit various types of artificial defects and several non-destructive tests have already been carried out by research center technicians to characterize the mechanical properties of the materials and the existing damages. The behaviors of several different data fusion approaches are compared here. Based on the presented analyses, it is demonstrated that fusion via support vector machines provided the most robust and consistent data fusion and defect detection capabilities.

Utilization of Wilcoxon-Mann-Whitney statistics in assessing the reliability of nondestructive evaluation technologies

Nondestructive evaluation (NDE) technologies are increasingly being used for the condition assessment of existing structures. In order to ensure best funding allocations and most effective maintenance and repair activities, the reliability of NDE technologies for a particular application needs to be carefully assessed to determine the margin of error (MOE) of NDE data. This research aims to investigate the utilization of “Wilcoxon-Mann-Whitney (WMW) Statistics” for the reliability assessment of NDE technologies. The reliability of three of the most widely used NDE technologies for the detection of subsurface cracking in concrete was assessed using WMW analysis. The NDE technologies assessed were: Impact Echo (IE), Infrared Thermography (IRT), and Ground Penetrating Radar (GPR). Two in-service bridge decks and two full-scale fabricated deck slabs were nondestructively tested to provide suitable data for analysis. The in-service bridge decks contained areas of mild and severe subsurface cracking as determined from previous testing performed by the Missouri Department of Transportation (MoDOT). The fabricated decks, on the other hand, contained areas of synthetic cracking of different sizes and depths. The reliability indices determined from WMW analysis for the three NDE technologies discussed in this manuscript are exactly equivalent to the indices determined previously by the same authors using Receiver Operating Characteristics (ROC) analysis. Results of WMW analysis, by the way, indicated that IE and IRT technologies were more reliable than GPR for the detection of concrete subsurface cracking. The final WMW indices of IE and IRT data were much higher than the corresponding indices of GPR data for both the in-service bridge decks and the fabricated slabs. The interesting equivalence between WMW and ROC analyses may encourage other researchers to utilize the former method in order to investigate the reliability of other NDE technologies in other applications. The findings of this research may also assist decision-makers considering the use of these NDE technologies in order to form decisions regarding maintenance and repair needs for bridges.