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"Ali, Nadir"
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Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models
Temperature is an important environmental factor for long-span bridges because it induces thermal loads on structural components that cause considerable displacements, stresses, and structural damage. Hence, it is critical to acquire up-to-date information on the status, sustainability, and serviceability of long-span bridges under daily and seasonal temperature fluctuations. This paper intends to investigate the effects of temperature variability on structural displacements obtained from remote sensing and represent their relationship using supervised regression models. In contrast to other studies in this field, one of the contributions of this paper is to leverage hybrid sensing as a combination of contact and non-contact sensors for measuring temperature data and structural responses. Apart from temperature, other unmeasured environmental and operational conditions may affect structural displacements of long-span bridges separately or simultaneously. For this issue, this paper incorporates a correlation analysis between the measured predictor (temperature) and response (displacement) data using a linear correlation measure, the Pearson correlation coefficient, as well as nonlinear correlation measures, namely the Spearman and Kendall correlation coefficients and the maximal information criterion, to determine whether the measured environmental factor is dominant or other unmeasured conditions affect structural responses. Finally, three supervised regression techniques based on a linear regression model, Gaussian process regression, and support vector regression are considered to model the relationship between temperature and structural displacements and to conduct the prediction process. Temperature and limited displacement data related to three long-span bridges are used to demonstrate the results of this research. The aim of this research is to assess and realize whether contact-based sensors installed in a bridge structure for measuring environmental and/or operational factors are sufficient or if it is necessary to consider further sensors and investigations.
Journal Article
Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques
Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.
Journal Article
Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data
Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods.
Journal Article
Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images
The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.
Journal Article
An Open Platform for RGB Composite Analysis and Validation: RGB_DIGI
In this paper we present an open platform which consists of a free automated digital image processing tool, a camera network portal where digital images are available freely, operational monitoring system and Cal / Val activities producing near real time results for comparison of satellite-derived products with webcam derived and in-situ data. We proposed a concept of RGB-based data platform which may provide some progresses to existing challenges of applications of digital imagery technologies. We provided a quick analysis from a set of webcam images and discuss their capabilities on capturing climate proxies in 10 years’ time.
Journal Article
LAI-Derived Atmospheric Moisture Condensation Potential for Forest Health and Land Use Management
The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = Ta − Td) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change.
Journal Article
Design and Fabrication of a Fast Response Resistive-Type Humidity Sensor Using Polypyrrole (Ppy) Polymer Thin Film Structures
In this research article, an organic polymer based polypyrrole (Ppy) composite material has been synthesized and analyzed for the design and fabrication purposes of a fast-responsive, highly sensitive, and an economical resistive-type novel humidity detection sensor. This humidity sensor most suitably serves the purpose for industrial humidity (i.e., values ranging from low to high) detection applications. First, a polypyrrole composite material (a mixture of polypyrrole, polypyrrole-NiO, polypyrrole-CeO2, and polypyrrole-Nb2O5) has been synthesized by chemical oxidative polymerization method, and then is treated at various temperatures, i.e., 100, 150 and 200 °C, respectively. After this treatment, the synthesized samples were then characterized by using FTIR, SEM, and DTA/TGA techniques for analyzing humidity sensing properties. The polypyrrole samples with the best morphological structure and properties were then incorporated on interdigitated electrodes. For the fabrication purposes of this thin film structure, at first a few drops of polyvinyl alcohol (PVA) were placed over interdigitated electrodes (IDE) and then the synthesized polypyrrole composite was uniformly deposited in the form of a thin film over it. The plots show that this is a good resistive-type humidity detection device for the relative humidity range of 30% to 90%. The response and recovery times of this newly fabricated humidity sensor were reported to be the same as 128 s at room temperature. Additionally, the stability and the repeatability response behavior of this Ppy sensor were verified up to five cycles of multiple repetitions. This presents an excellent stability and repeatability performance of the sensor. Furthermore, the capacitances versus humidity response and recovery properties of the designed sensor were studied too. This illustrates an excellent capacitive verses humidity response and shows a linear and an active behavior. Lastly, the experimental result proves that polypyrrole composite thin film shows a reasonable best performance up to a temperature of 100 °C.
Journal Article
Adult ileocecal intussusception as an unusual presentation of ascending colon adenocarcinoma: a case report from Sudan
Adult colonic intussusception, is a rare entity that is typically associated with underlying organic pathologies, notably colorectal tumors, unlike pediatric cases, which are mostly idiopathic. We present a unique case of a 42-year-old female with ascending colon adenocarcinoma, where ileocecal intussusception served as the initial clinical manifestation. The patient’s non-specific symptoms, familial history of colon cancer and subsequent diagnostic evaluations underscore the importance of considering malignancy in such presentations. Successful laparoscopic right hemicolectomy resolved the intussusception. This case, which is the first case to be reported in Sudan, highlights the clinical complexities of adult colonic intussusception, emphasizing the need for a heightened index of suspicion for underlying malignancy and the significance of timely surgical intervention. Furthermore, the challenges encountered in resource-limited settings underscore the necessity for genetic testing to guide familial screenings and identify hereditary factors contributing to colon cancer, providing valuable insights for clinicians managing similar cases.
Journal Article
Laparoscopic exploration of a wandering spleen in a complex adolescent case with sigmoid volvulus and left-side portal hypertension: a case report
Wandering spleen (WS) is a rare condition characterized by the hypermobility of the spleen due to the absence or abnormal flexibility of suspensory ligaments. We present a 16-year-old female presented with intermittent abdominal pain, constipation, and a palpable mass in the right iliac fossa. Imaging revealed a WS associated with sigmoid volvulus and portal hypertension. Despite a decade of symptoms, the patient remained undiagnosed. Laparoscopic splenectomy was performed successfully, addressing both WS and sigmoid volvulus. The patient’s symptoms resolved, and she was discharged in good condition. This case emphasizes the need for clinical awareness of WS in the differential diagnosis of abdominal pain. It highlights the role of imaging in prompt diagnosis and the necessity of surgical intervention. Our case sheds light on the association of WS with other conditions, providing clinicians with valuable insights for effective management.
Journal Article