Key Engineering Materials Vol. 1033

Abstract: This study presents a comparative evaluation of two widely used chemical analysis techniques: scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) and spectrophotometry. SEM-EDX is renowned for its ability to provide qualitative and quantitative elemental analysis at microscopic levels, making it a powerful tool for material characterization. On the other hand, optical emission spectroscopy, which analyses the light emitted by excited atoms, is highly effective for the rapid and precise quantification of elements in various sample types, especially metals and alloys. The research aims to assess the effectiveness, accuracy, and applicability of these techniques, by analysing identical samples (welding wire) using both SEM-EDX and spectroscopy, this study highlights the strengths and limitations of each method. Key parameters such as sensitivity, detection and data interpretation are compared to provide a comprehensive understanding of their performance in chemical analysis.

Abstract: The increasing complexity of X-ray test data and the demand for precise, large-scale evaluations have exposed the limitations of manual flaw detection methods in Non-Destructive Testing (NDT). Traditional manual approaches, while widely used, are labor-intensive and prone to human error, often leading to inconsistencies and inefficiencies. An Artificial Intelligent driven (AI-driven) system has been developed to automate the analysis and evaluation of X-ray test data, improving both accuracy and efficiency. The system employs advanced image segmentation and classification algorithms, aligning with ISO standards to detect defects in welded structures. By reducing reliance on manual interpretation, the system enhances the reliability and speed of the evaluation process, while also allowing for expert oversight through manual corrections when necessary. Integration with a Laboratory Information Management System (LIMS) ensures streamlined data handling and traceability, minimizing human error in data recording. As the AI model processes more data, it continuously improves, adapting to evolving defect patterns and maintaining high performance. This paper details the system’s AI architecture, the methodology employed for X-ray image analysis, and the performance results from industrial applications, demonstrating how this technology addresses key challenges in NDT processes.

Abstract: The assessment of compressive strength, and air-dry density constitutes essential parameters for evaluating the quality and performance of earthen construction materials. To ascertain these properties, this study investigates the potential application of ultrasonic testing as a non-destructive evaluation technique for earthen materials, including specimens, elements, or structures. The methodology is predicated on the measurement of ultrasonic pulse velocity (UPV), which is affected by various factors such as density, elasticity, and the curing process. By examining the propagation of ultrasonic waves through earthen samples, significant insights can be obtained regarding their drying duration, compressive strength, and density. Compressive strength is a pivotal factor in evaluating the structural integrity of earthen materials. The UPV method provides a non-destructive means to ascertain the compressive strength of earthen samples, thereby serving as a valuable instrument for quality control and assessment of earthen construction materials. Density, another critical property influencing the performance of earthen materials, can also be evaluated using the UPV method. By measuring ultrasonic pulse velocity and analyzing its correlation with density, this non-destructive approach enables rapid and efficient estimation of the compactness and quality of earthen mixtures. The ultrasonic method presents a non-destructive and efficient strategy for determining the compressive strength, and density of various soil compositions. By quantifying pulse velocity and examining its relationship with these properties, substantial insights can be garnered regarding the quality and performance of earthen construction materials. This technique holds the potential to enhance assessment and quality control processes in earthen construction, ultimately contributing to the development of more sustainable and reliable structures utilizing earthen techniques.

Abstract: Automating failure detection of infrastructure is essential to avoid unexpected downtime, which could impact both operations and user safety. However, frequent replacement of laminated rubber bearing (LRB) as a vital seismic isolation component can be inefficient and inspecting them for faults is often labor-intensive. This is a significant challenge in maintaining structural integrity, especially in critical infrastructure where continuous monitoring is necessary. Recent innovation in deep learning (DL) provides a promising alternative to traditional inspection methods, offering more efficient and accurate assessments. Hence, this paper explores the practical application of DL for detecting internal failure in laminated rubber bearings installed in structures. Neural network models, including a convolutional neural network (CNN), long-short term memory (LSTM), and their combinations (hybrid CNN-LSTM), were employed. An experimental setup was developed to simulate a bridge structure supported by down-scale LRB samples at its base. Samples with internal debonding failure were manufactured by reducing the bonding adhesive, to replicate failure conditions due to shear loading where the LRBs were forced to slide in an extreme condition. The vibration platform was actuated under different levels of frequency. Both Healthy and Faulty LRB conditions data were collected for 10 minutes each, which is adequate for 10 sets of data divided into training, validation, and testing with a fixed 6:2:2 ratio, respectively. Results revealed that CNN outperformed the other two models in average classification accuracy at 5Hz and 10Hz with 97.65% and 91.45%, respectively. Plus, CNN recorded the shortest training period among all models compared, with only 128 seconds at 15Hz, compared to 695 seconds and 1599 seconds owned by LSTM and hybrid CNN-LSTM respectively. In conclusion, neural networks have shown the capability in identifying LRB internal failure. CNN has the advantage in terms of both classification accuracy and training period compared to LSTM and hybrid CNN-LSTM models.