Wael A. Altabey

Under thermo-mechanical loading via isothermal bulging pressure (IBP), composite circular membranes (CCMs) exhibit highly temperature-dependent mechanical behavior, characterized by distinct variations in Young’s modulus, yield strength, and residual stress. Subjecting the membranes to varied pressure and temperature profiles induces controlled deformation, enabling the characterization of composite material properties particularly for components deployed in microelectromechanical systems (MEMS) operating under extreme conditions. This study investigates the thermo-mechanical response of CCMs fabricated from basalt fiber-reinforced polymer (BFRP) subjected to thermal and mechanical stress utilizing the IBP methodology, focusing on diverse engineering and bioengineering sensor applications. In the experimental protocol, the membrane is initially pre-stressed and rigidly clamped between two fixtures. The application of a differential pressure subsequently induces out-of-plane bending. To characterize these thermo-mechanical phenomena, an analytical framework based on the IBP approach is developed to describe membrane behavior across both elastic and plastic regimes. Concurrently, a finite element model (FEM) is implemented to evaluate the analytical results and assess the specific influence of initial pre-stressing on the pressure tests. To comprehensively map the mechanical response under load, parametric variations in membrane thickness and constituent material configurations are systematically evaluated. Furthermore, the temperature dependence of the material properties is analyzed to isolate its effect on the overall thermo-mechanical response. The relative error ( E r ) for the maximum deflection ( w max ) between the analytical and numerical results remains below 0.3%. The analytical computations and numerical simulations are executed using MATLAB, the MATLAB PDE Toolbox, and ANSYS. Finally, the findings demonstrate strong agreement with established empirical data from the literature, thereby validating the reliability, accuracy, and robust applicability of the proposed identification technique.

In recent decades, the rapid development of transportation infrastructure safety, such as highways, bridges, and tunnels has greatly promoted the development of the regional economy. The structures with safety hazards and emergencies need continuous monitoring over time. The integrated artificial intelligence algorithms with sensor responses can provide real-time information for further analysis and decision-making for the transportation system, improving the circulation efficiency of the transportation network, ensuring the stability of road structures, and avoiding irreparable damage. This paper aims to develop an efficient and low-cost method to help detect early-stage transportation infrastructure damage through permanent or periodic monitoring. In this research, we used LiDAR scanning units (terrestrial LiDAR fixed on holders and movable units fixed on UAVs) integrated with a novel deep neural network (DNN) for structural monitoring of bridges based on the 3D mapping of bridge displacement compiled from LiDAR scanning over time. The monitoring model is based on a recurrent neural network with long short-term memory blocks (RNN-LSTM) since the LiDAR scanning datasets have a time-dependent and memory-dependent behavior. The response of the proposed DNN achieved a high accuracy rate, regression rate, and F-score equal to 96.43%, 93.77%, and 91.65%, respectively. A deep analysis of the confusion matrix and a side-by-side look at predicted and actual conditions highlight how well the model can tell apart different traditional methods to estimate the bridge displacement in literature. So, the data from LiDAR and DNN models can be combined to analyze the monitoring of transportation infrastructure.

Under thermo-mechanical stress via a bulge test (BT), composite circular diaphragms (CCD) exhibit temperature-dependent mechanical behavior, including changes in Young’s modulus, yield strength, and residual stress. The application of a differential pressure and temperature causes the membrane to deform, allowing researchers to characterize composite material properties, particularly for materials used in microelectromechanical sensors (MEMS) operating in harsh environments. This paper aims to explore how CCD made from basalt fiber reinforced polymer (BFRP) behaves under thermal and mechanical stress, particularly in various engineering and bioengineering sensor applications, using a technique known as the BT. To start, the diaphragm is pre-stressed and clamped between two plates. When applying differential pressure, it causes the diaphragm to deform. An analytical approach is developed for utilizing the BT to describe the thermo-mechanical properties of these diaphragms. This method is well-suited for examining how diaphragms behave mechanically in both elastic and plastic states. A finite element model (FEM) is extended to analyze the BT outcomes and look into how pre-stress influences the pressure testing, comparing results from the FEM with those derived from analytical calculations. The variations in thickness and material type are also taken into account to better understand how they affect the diaphragm’s mechanical behavior under stress. Additionally, this work considers how temperature impacts the material properties of the diaphragm, which is crucial for analyzing its thermo-mechanical response. The relative () for maximum deflection the analytical and numerical results is less than 0.3%. The simulations are done using ANSYS, MATLAB and its PDE toolbox to get the results.

Understanding the mechanical properties of diaphragms is crucial for the reliability and performance of Nano Electro Mechanical Sensors (NEMS). There are various methods documented for characterizing micro materials. One well-established nondestructive approach is the bulge test, which is effective for examining the mechanical traits of diaphragms. This study aims to enhance NEMS performance by exploring how a Nano rectangular diaphragm (NRD), created from a new material within NEMS, behaves mechanically through an improved bulge test technique. The NRD is made from layers of basalt fiber reinforced polymer (BFRP) laminate composites, sized at the nanoscale, and was tested at room temperature for plane-strain bulging. Before applying any loads, the NRD is pre-stressed to eliminate any initial deflection and then securely clamped between two plates. A differential pressure is applied, causing deformation in the laminated composite NRD. This setup makes the plane-strain bulge test well-suited for analyzing the mechanical properties of the laminated composite NRD in both elastic and plastic states. We derived an exact solution to the governing equations for the symmetric cross-ply BFRP laminated composite NRD, taking into account the impact of residual strength from the pre-stressed condition. The relationship between stress and strain for the BFRP laminated composite NRD was determined through hydraulic bulging tests, and we analyzed the gradual thickness changes at different points of the hemisphere formed during the bulge test. We can also extend a finite element model (FEM) to analyze the BT outcomes and look into how pre-stress influences the pressure testing, comparing results from the FEM with those derived from analytical calculations. The relative error (E_r) for w_max between the analytical and numerical results is less than 0.336%. We ran simulations using ANSYS, MATLAB and its PDE toolbox to get our results.

The goal of this paper is to improve the monitoring of civil structures when we pair unmanned aerial vehicles (UAVs) technology with the current proposed algorithm, particularly to identify cracks in concrete structures. Typically, the current UAV methods are more about creating state maps of these structures, but they struggle with the impact of the drone’s movement on crack detection accuracy. This presents challenges for using intelligent systems for concrete crack detection. The current approach combines advanced technologies with a network of high-definition cameras mounted on inspection UAV systems and distributed in different parts of the structure’s surface, and smart processing methods to keep tabs on structure health accurately and in real-time. By merging a novel Convolutional Neural Network (CNN) with a specialized technique called the Crack Contour Network (CCN) to extract features, we boost the accuracy and reliability of our assessments for target identification, the type, location, and dimensions of cracks, including their borders. After collecting those images using a high-definition camera-based UAV system, they preprocessed the images by cropping and segmenting the crack images, standardizing their size, adjusting the contrast, enhancing the image data to boost the sample count, and creating a database with these labeled samples. Once that’s done, use this database for training the CNN-CCN, which was built and tuned to the parameters of the network. Finally, the network tested that’s been trained, and the algorithm will output the detection results for the cracks. An algorithm CNN-CCN uses metrics like Intersection over Union (IoU), accuracy rates, regression rates, and F1-score to evaluate its performance in crack identification and determining the crack’s contour area. The results indicate that the proposed CNN-CCN network performs well, achieving high IoU for crack detection, accuracy rates of 96.43%, regression rates of 93.77%, and an F1-score of 91.65%, alongside a training time of 75 s and a model size of 199 KB. A deep analysis of the confusion matrix and a side-by-side look at predicted and actual conditions highlight how well the model can tell apart different traditional algorithms in the literature. This technique allows for automatic high-dimensional extraction and complex features from infrastructures, leading to reduced interference and errors in detection, which enhances the clarity and overall effectiveness of civil structure assessments.

The study of long-term pavement performance is a fundamental topic in the field of highway engineering. Through comprehensive and in-depth research on the pavement system, the previous scattered, one-sided, superficial, and perceptual knowledge and experience are summarized and sublimated into a systematic and complete engineering theory, thereby providing powerful guidance and assistance for the practice of pavement design, construction, maintenance, operation, and management. In this research, the mentoring system deployment technology for automatic monitoring is carried out for long-term pavement performance. By burying a variety of sensors in different parts of the road surface, base, roadbed, slope, etc., a sensor monitoring network based on the Internet of Things technology is formed to achieve accurate, reliable, and continuous observation of environmental meteorology, physical state, mechanical response, structural deformation, and other indicators. The large amount of data and high real-time requirements mean that the perception data collected from sensors, including temperature, humidity, pressure, asphalt strain, and displacement, can be used to train a deep learning model based on a Convolutional Neural Network (CNN) algorithm. This model predicts multi-point pavement displacement to detect damage such as asphalt cracks and potholes. The response of the proposed CNN achieved a high accuracy rate, regression rate, and F-score equal to 87.24%, 84.12%, and 85.96%, respectively. This work highlights the potential of using a variety of sensors to aid deep learning algorithms for monitoring long-term pavement performance.

Long-term pavement performance is a key topic in highway engineering. By diving deep into research on pavement systems, we can bring together past, fragmented knowledge and experiences into a solid, comprehensive engineering theory. This essentially helps guide practical work like pavement design, construction, maintenance, and management. In this research, we look at using a mentoring system for automatic monitoring of pavement performance. By placing various sensors in different positions like the road surface, base, and slopes, a sensor network powered by Internet of Things technology is created. This setup allows for accurate and ongoing observation of factors like weather, physical condition, mechanical responses, and structural changes. Given the large volume of data and the need for real-time analysis, a data from sensors measuring temperature, humidity, pressure, asphalt strain, and displacement are used to train a deep learning model based on a Convolutional Neural Network (CNN) algorithm. This model helps predict multi-point displacement in the pavement, which allows us to detect issues like pavement damage. Impressively, the CNN model achieved accuracy, regression rates, and F-score of 93.51%, 91.63%, and 90.64% respectively. To improve the experimental section of a deep learning study, we compared the performance of the proposed model against several established or simpler algorithms (baselines) in the literature such as K-Nearest Neighbors (K-NN), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM). This contextualizes the model's efficacy and demonstrates its advantage over existing methods. This study showcases how different sensors can support deep learning algorithms in the assessment of pavement performance over the long term.