Reinforced concrete (RC) box girders are a common structural member for road bridges in modern construction. The hollow cross-section of a box girder is ideal in carrying eccentric loads or torques introduced by skew supports. This study employed acoustic emission (AE) monitoring on multicell RC box girder specimens subjected to laboratory-based torsion loading. Three multicell box girder specimens with different cross-sections were tested. The aim is to acquire AE analysis data indicative for characterizing torsion fracture in the box girders. It was demonstrated through appropriate parametric analysis that the AE technique could be utilized to effectively classify fracture developed in the specimens for describing their mechanical behavior under torsion. AE events localization was presented to illustrate the trend of crack and damage propagation in different stages of fracture. It could be observed that spiral-like patterns of crack were captured through AE damage localization system and damage was quantified successfully in different stages of fracture by using smoothed b-value analysis.
Little is known about the spatial and temporal distribution of blast fishing which hampers enforcement against this activity. We have demonstrated that a triangular array of hydrophones 1 m apart is capable of detecting blast events whilst effectively rejecting other sources of underwater noise such as snapping shrimp and nearby boat propellers. A total of 13 blasts were recorded in Sepangor bay, North of Kota Kinabalu, Sabah, Malaysia from 7th to 15th July 2002 at distances estimated to be up to 20 km, with a directional uncertainty of 0.2 degrees . With such precision, a network of similar hydrophone arrays has potential to locate individual blast events by triangulation to within 30 m at a range of 10 km.
Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.
Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.