Adverse environmental conditions result in corrosion during the life cycle of marine structures such as pipelines, offshore oil platforms, and ships. Generalized corrosion leading to the loss of wall thickness can cause the degradation of the integrity, strength, and load bearing capacity of the structure. Nondestructive detection and monitoring of corrosion damage in difficult to access areas can be achieved using high-frequency guided waves propagating along the structure. Using standard ultrasonic wedge transducers with single-sided access to the structure, specific high-frequency guided wave modes (overlap of both fundamental Lamb wave modes) were generated that penetrate through the complete thickness of the structure. The wave propagation and interference of the guided wave modes depend on the thickness of the structure and were measured using a noncontact laser interferometer. Numerical simulations using a two-dimensional finite element model were performed to visualize and predict the guided wave propagation and energy transfer across the plate thickness. During laboratory experiments, the wall thickness was reduced uniformly by milling of one steel plate specimen. In a second step, wall thickness reduction was induced using accelerated corrosion for two mild steel plates. The corrosion damage was monitored based on the effect on the wave propagation and interference (beating effect) of the Lamb wave modes in the frequency domain. Good agreement of the measured beatlengths with theoretical predictions was achieved, and the sensitivity of the methodology was ascertained, showing that high-frequency guided waves have the potential for corrosion damage monitoring at critical and difficult to access locations.

Steel structures with bolted joints are easily dismantled and repurposed. However, maintaining joint integrity is a challenge. This paper reports a non-destructive methodology to monitor steel bolted joints. Piezoelectric ceramic patches have been surface bonded in the joint for transmission and reception of guided ultrasonic waves. Both single and multiple bolted joints have been investigated. It has been demonstrated that the variation in acoustic impedance due at the bolt interface can be discerned and calibrated with bolt torque level. The recorded reflections from interfaces are used as inputs for a newly developed imaging algorithm. The proposed method has the potential to be a reference-free and fully automated method.

Pipelines are the primary means of land transportation of oil and gas globally, and pipeline integrity is, therefore, of high importance. Failures in pipelines may occur due to internal and external stresses that produce stress concentration zones, which may cause failure by stress corrosion cracking. Early detection of stress concentration zones could facilitate the identification of potential failure sites. Conventional non-destructive testing (NDT) methods, such as magnetic flux leakage, have been used to detect defects in pipelines; however, these methods cannot be effectively used to detect zones of stress concentration. In addition, these methods require direct contact, with access to the buried pipe. Metal magnetic memory (MMM) is an emerging technology, which has the potential to characterize the stress state of underground pipelines from above ground. The present paper describes magnetic measurements performed on steel components, such as bars and tubes, which have undergone changing stress conditions. It was observed that plastic deformation resulted in the modification of measured residual magnetization in steels. In addition, an exponential decrease in signal with the distance of the sensor from the sample was observed. Results are attributed to changes in the local magnetic domain structure in the presence of stress but in the absence of an applied field.

This study presents a method of ultrasonic flaw identification using phased array ultrasonic inspection data. Raw data from each individual channel of the phased array ultrasonic inspection are obtained. The data trimming and de-noising are employed to retain the data within the boundary of the inspected object and remove the speckle noise components from the raw data, respectively. The resulting data are passed into a sequence of signal processing operations to identify embedded flaws. A shape-based filtering method is proposed to reduce the intensity of geometric noise components due to the non-uniform microstructures introduced in the manufacturing process. The resulting data matrices are integrated to obtain the intensity matrix of the possible flaw regions. Thresholding is applied to the intensity matrix to obtain the potential flaw regions, followed by a connected component analysis to identify the flaws. The overall method is demonstrated and validated using realistic phased array experimental data.

In this paper, the time-varying autoregressive (TVAR) model is integrated with the K-means—clustering technique to detect the damage in the steel moment-resisting frame. The damage is detected in the frame using nonstationary acceleration response of the structure excited using ambient white noise. The proposed technique identifies and quantifies the damage in the beam-to-column connection and column-to-column splice plate connection caused due to loosening of the connecting bolts. The algorithm models the nonstationary acceleration time history and evaluates the TVAR coefficients (TVARCs) for pristine and damage states. These coefficients are represented as a cluster in the TVARC subspace and segregated and classified using K-means—segmentation technique. The K-means—approach is adapted to simultaneously perform partition clustering and remove outliers. Eigenstructure evaluation of the segregated TVARC cluster is performed to detect the temporal damage. The topological and statistical parameters of the TVARC clusters are used to quantify the magnitude of the damage. The damage is quantified using the Mahalanobis distance (MD) and the Itakura distance (ID) serving as the statistical distance between the healthy and damage TVARC clusters. MD calculates a multidimensional statistical distance between two clusters using the covariance between the state vectors, whereas ID measures the dissimilarity of the autoregressive (AR) parameter between reference state and unknown states. These statistical distances are used as damage-sensitive feature (DSF) to detect and quantify the initiation and progression of the damage in the structure under ambient vibrations. The outcome of both the DSFs corroborate with the experimental investigation, thereby improving the robustness of the algorithm by avoiding false damage alarms.

This paper focuses on the development and validation of a robust framework for surface crack detection and assessment in steel pipes based on measured vibration responses collected using a network of piezoelectric (PZT) wafers. The pipe structure considered in this study contained multiple progressive cracks occurring at different locations and with various orientations (along the circumference or length). The fusion of data collected from multiple PZT wafers was investigated based on two approaches: (a) combining the raw data from all sensors before establishing a statistical model for damage classification and (b) combining the features from each sensor after applying a multiclass support vector machine recursive feature elimination (MCSVM-RFE), for dimensionality reduction, and taking the union of discriminative features among the different sources of data. A MCSVM learning algorithm was employed to train the data and generate a statistical classifier. The dataset consisted of ten classes, consisting of nine damage cases and the healthy state. The accuracy of the prediction based on the two fusion approaches resulted in a high accuracy, exceeding 95%, but the number of features needed to enrich the accuracy (95%) differed between the two approaches. Furthermore, the performance and the precision in the prediction of the classifier were evaluated when the data from only a single sensor was used compared with the combined data from all the sensors within the network. Very promising results in the classification of damage were obtained, based on the case study that included multiple damage scenarios with different lengths and orientations.