Structural health monitoring for jacket-type offshore wind turbines: experimental proof of concept

Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier.Peer ReviewedPostprint (published version

Autom Constr

Field or laboratory data collected for work-related musculoskeletal disorder (WMSD) risk assessment in construction often becomes unreliable as a large amount of data go missing due to technology-induced errors, instrument failures or sometimes at random. Missing data can adversely affect the assessment conclusions. This study proposes a method that applies Canonical Polyadic Decomposition (CPD) tensor decomposition to fuse multiple sparse risk-related datasets and fill in missing data by leveraging the correlation among multiple risk indicators within those datasets. Two knee WMSD risk-related datasets-3D knee rotation (kinematics) and electromyography (EMG) of five knee postural muscles-collected from previous studies were used for the validation and demonstration of the proposed method. The analysis results revealed that for a large portion of missing values (40%), the proposed method can generate a fused dataset that provides reliable risk assessment results highly consistent (70%-87%) with those obtained from the original experimental datasets. This signified the usefulness of the proposed method for use in WMSD risk assessment studies when data collection is affected by a significant amount of missing data, which will facilitate reliable assessment of WMSD risks among construction workers. In the future, findings of this study will be implemented to explore whether, and to what extent, the fused dataset outperforms the datasets with missing values by comparing consistencies of the risk assessment results obtained from these datasets for further investigation of the fusion performance.CC999999/ImCDC/Intramural CDC HHS/United States2021-04-23T00:00:00Z33897107PMC8064735956

An In-Depth Investigation of the Effects of Work-Related Factors on the Development of Knee Musculoskeletal Disorders among Construction Roofers

Construction roofers have the uppermost likelihood of developing knee musculoskeletal disorders (MSDs). Roofers spend more than 75% of their total working time being restricted to awkward kneeling postures and repetitive motions in a sloped roof setting. However, the combined effect of knee-straining posture, roof slope and their association to knee MSDs among roofers are still unknown. This dissertation aimed to provide empirical evidence of the effects of two roofing work-related factors namely, roof slope and kneeling working posture, on the development of knee MSDs among construction roofers. These two factors were assessed as potential to increase knee MSD risks in roofing by evaluating the awkward knee rotations and heightened activation of knee postural muscles that might occur in sloped-shingle installation. Moreover, a novel ranking-based ergonomic risk analysis method was developed to identify the riskiest working phase in the sloped-shingle installation operation. In addition, a data fusion method was developed for treating multiple incomplete experimental risk related datasets that would affect the accuracy of risk assessments due to human and technology-induced errors during experimental data collection. The findings revealed that roof slope, working posture and their interaction have significant impacts on developing knee MSDs among roofers. Knees are likely to have increased exposure to MSD risks during placing and nailing shingles on sloped roof surfaces. The established data fusion method has been proven feasible in handling up to 40% missing data in MSD risk-related datasets. The contributions lie in enhanced understanding of the physical risk exposures of roofers\u27 knee MSDs and creation of the ranking-based ergonomic analysis method and the fusion method that will help improve the MSD risk assessment in construction. In the long run, these outcomes will help develop new knee joint biomechanical models, effective interventions, and education and training materials that will improve the workplace to promote health and safety of roofers

A tensor-based structural damage identification and severity assessment

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors

A Tensor-Based Structural Damage Identification and Severity Assessment

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors