1,721,007 research outputs found
Integration of Textural and Material Information into BIM Using Spectrometry and Infrared Sensing
No full textA Master of Science thesis in Civil Engineering by Asem Ahmad Zabin entitled, "Integration of Textural and Material Information into BIM Using Spectrometry and Infrared Sensing," submitted in May 2015. Thesis advisor is Dr. Tarig Ali. Soft and hard copy available.As-built data and drawings are essential documents that contain object dimensions, locations, materials, and other important information used by different parties during the processes of the building design, construction, and operation in order to perform commissioning or energy analysis, and preventive and corrective maintenance. These documents go through many changes and updates throughout the construction phase and after the handover, and this process is currently tracked manually which is time consuming. In the Architecture, Engineering, and Construction (AEC) industry, Building Information Modeling is increasingly used throughout a facility's life cycle for various applications, such as planning, conceptual design, detailing, fabrication, renovations, space usage planning, and managing building maintenance. For existing buildings, as-built Building Information Models (BIMs) are often constructed using dense, three dimensional (3D) point clouds data obtained from laser scanners. Laser scanners can quickly detect and capture the "as-is" conditions of a structure, and then the points get processed to obtain the 3D geometric model. Traditionally, as-built BIMs do not have material and textural information of the buildings integrated into them. This thesis presents a methodology for generation of textural and material rich as-built BIM Models. The proposed method used thermal infrared sensing to capture thermal images of the interior walls of an existing building. These images were then processed and only walls' features were extracted using a segmentation algorithm. The digital numbers of resulted images were then transformed into radiance values that represent the emitted thermal infrared radiation recorded at each pixel of the interior walls images. These radiance values were used to extract textural information from the images. Statistical correlations between these values and models of interior gypsum and concrete were obtained through a Monte Carlo simulation approach and further used to extract material information from the images. The extracted texture and material information were then integrated in the BIM Model, providing the data needed for the assessment of building conditions in relation to energy efficiency and water and waste water systems leaks.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Pavement Crack Assessment Using Satellite Remote Sensing and Deep Learning Model
No full textA Master of Science thesis in Civil Engineering by Maryam Al Adab entitled, “Pavement Crack Assessment Using Satellite Remote Sensing and Deep Learning Model”, submitted in July 2025. Thesis advisor is Dr. Tarig Ali. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Cracking is one of the common forms of surface distress found in asphalt pavement. It can affect riding quality, structural integrity, and decreases the design life of the pavement. Accurate and timely detection of pavement cracks is crucial for maintenance planning. There are several methods used for assessing pavement condition, and the most common methods include the traditional visual inspection or vehicle-mounted system. However, these methods remain to be time consuming and challenging for large urban networks. Recent advancement is high-resolution satellite imagery and deep learning provide new opportunities for efficient and large-scale pavement condition assessment. This research investigates the feasibility of using high-resolution satellite imagery, combined with deep learning model to detect and classify asphalt pavement crack in the urban context of Los Angeles (LA). LA was selected due to extensive road networks and visible surface distresses pattern that can be captured by satellite. This study uses high-resolution satellite imagery obtained through Google Earth Pro. To achieve this goal, the YOLOv8s-seg model, an advanced variant of the YOLO (You Only Look Once) family, was fine-tuned on a manually labeled crack dataset for realtime object detection and segmentation. The training dataset includes three main types of cracks: alligator cracks, Longitudinal and transverse cracks, and sealed cracks. Images were carefully annotated using the Roboflow platform and augmented to increase data diversity and improve model generalization.The results obtained demonstrate the potential of this approach for automatically detecting and classifying different cracks types from satellite imagery, despite the challenges posed by satellite resolution limits and background noise. The study is among the first to explore pavement crack type classification at a city scale using accessible satellite data and contributes a practical workflow for integrating AI-based surface distress assessment into pavement management strategies. The finding highlights how remote sensing and DL support cost-effective, scalable assessment of asphalt pavement conditions in major cities like Los Angeles.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Development of a Smart System for Leak Detection in Water Distribution Networks
No full textA Master of Science thesis in Civil Engineering by Doha Hesham Elshazly entitled, “Development of a Smart System for Leak Detection in Water Distribution Networks”, submitted in December 2021. Thesis advisor is Dr. Tarig A. Ali and thesis co-advisor is Dr. Md. Mortula. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Water is a critical source of life for the human society, which needs to be sustained. Every year, a considerable portion of water is lost from water distribution networks (WDNs) due to leaks. This is accompanied by significant consequences including, economic losses and supply disruption, which are major concerns for water utility companies. Hence, a robust and timely leak detection method is necessary to conduct corrective maintenance so as to minimize the underlying consequences. Previous studies have investigated the efficiency and effectiveness of various nondestructive detection methods including ground penetrating radar (GPR), spectrometry, and infrared sensing. Based on the outcome of the recent research on leak detection using infrared sensing, this study aims to develop a smart leak detection system that integrates remote sensing and geographic information system (GIS). The proposed system is to be used by a drone, equipped with an infrared (IR) camera, and geospatial analysis tools that incorporate remote sensing and GIS for near-real time leak detection. In addition, this study utilizes currently installed pressure, flow and water quality monitoring sensors in WDNs. A GIS customized interface was developed to automate the leak detection system and allow for timely data processing. The proposed system was validated on a section of the WDN in the city of Sharjah, in collaboration with Sharjah Electricity and Water Authority (SEWA). The results obtained in this research proved the efficiency of the proposed method for leak detection in WDNs.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Microplastic Pollution Detection in Coastal Waters of Dubai
No full textA Master of Science thesis in Civil Engineering by Batoul Mohsen entitled, “Microplastic Pollution Detection in Coastal Waters of Dubai”, submitted in November 2020. Thesis advisor is Dr. Md. Maruf Mortula and thesis co-advisor is Dr. Tarig Ali. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Study of the Impact of the Climate and Land-use/Land-cover (LULC) Changes on the UAE Mangrove Forests over the Period 1990-2020
No full textA Master of Science thesis in Civil Engineering by Asif Raihan entitled, “A Study of the Impact of the Climate and Land-use/Land-cover (LULC) Changes on the UAE Mangrove Forests over the Period 1990-2020”, submitted in June 2023. Thesis advisor is Dr. Tarig Ali and thesis co-advisor is Dr. Md. Maruf Mortula. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Mangroves are integral part of coastal ecosystem, demonstrating resiliency against adverse anthropogenic and environmental effects. They provide blue carbon stock and security against coastal erosion and act as a nutrient source for many aquatic inhabitants along with providing raw materials for human consumption. However, mangroves continue to perish due to climate factors and land-use/land-cover (LULC) changes. Past research has indicated some environmental factors influencing the growth and sustenance of mangrove forests. This thesis aims at studying the impact of the climate and LULC changes on the UAE mangrove forests during the period 1990-2020. The studied climate change related factors include surface temperature, sea-level rise, salinity, and coastal inundation. LULC changes were assessed by creating LULC maps over the period of study through supervised classifications of Landsat images. Then, remote sensing techniques and supervised machine learning methods were utilized to derive the climatic factors. The correlations between mangrove and these factors were investigated. Finally, forest-based classification and regression analysis was conducted to study the impact of land surface temperature, vegetation extent, slope, and salinity on mangrove biomass. It was found that Land Surface Temperature is closely related to mangrove and that mangrove biomass was highest in the land surface temperature range 30-35 °C. LULC changes showed a positive correlation with total vegetation cover (i.e., including mangrove) in terms of area. Results have not shown clear correlation between tidal inundation and mangrove. Furthermore, results showed that mangrove biomass was negatively affected by sea-level rise. In the study area, analysis showed that mangroves tended to thrive in flat slope (e.g., around 2%). The analysis results showed a general positive relationship between mangrove and salinity corresponding to increase in mangrove biomass, however site-specific analysis showed an increase in mangrove density with decrease in salinity. A model with coefficient of determination is 0.85 is developed in this thesis for the relationship between land surface temperature, slope, salinity, and mangroves using random forest classification and regression analysis.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Development of a Machine Learning Based Smart Water Leak Detection System
No full textA Master of Science thesis in Civil Engineering by Rabab Al Hassani entitled, “Development of a Machine Learning Based Smart Water Leak Detection System”, submitted in December 2024. Thesis advisor is Dr. Md. Maruf Mortula AND thesis co-advisor is Dr. Tarig Ali. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Spatial Distribution and Abundance of Emerging Microplastics Pollutants in Groundwater of UAE
No full textA Master of Science thesis in Civil Engineering by Bushra Tatan entitled, “Spatial Distribution and Abundance of Emerging Microplastics Pollutants in Groundwater of UAE”, submitted in June 2023. Thesis advisor is Dr. Md. Mortula and thesis co-advisor is Dr. Tarig Ali. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Global production of plastics is expected to keep on increasing as consumption increases. Plastics are known to undergo disintegration in the environment resulting in the formation of microplastics. Microplastics pollution is growing, but few studies have considered assessment of contamination in groundwater. This might be due to lack of monitoring groundwater for microplastics contamination. However, groundwater is a common source of water in various industries globally. In the UAE, more than half of the water supply is from groundwater, specifically in the agricultural industries. This study aimed to investigate microplastics contamination in groundwater in the UAE. The study identified 30 groundwater boreholes in the Rahmaniya, Bedee, and Falah regions of Sharjah, UAE, from which samples were collected. To prepare the samples, a series of pretreatment procedures involving 30% hydrogen peroxide, density separation, and extraction filters were employed. Microplastics were subsequently detected using a microscope with 40x magnification, revealing the presence of microplastics in the water of 11 boreholes in Rahmaniya, ranging from 12 to 235 n/L, respectively. In the Falah area, contamination was observed in two boreholes, with 56 and 41 n/L, respectively, while no contamination was found in the Bedee area. Characterization of microplastics was conducted using ATR-FTIR analysis, which has successfully matched the obtained spectra with polyethylene terephthalate, polyethylene, and polypropylene for 10 samples. To examine the spatial variability and clustering of microplastics, a raster layer was created in GIS and used to perform hotspot analysis. The findings demonstrated a significant hotspot in the Rahmaniya area, indicating concentrated microplastics contamination. Moreover, potential sources of contamination were investigated based on land use and location, and three remote sensing-based indices, which are the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) and moisture index. This has helped in the identification of industrial areas, the Sajaa landfill, Bedee Farmland, and the water dump lagoon as potential sources of contamination.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Study of the Impact of Land Use and Land Cover (LULC) Changes on local climate in Six Mega Global Cities in the 21st Century
No full textA Master of Science thesis in Civil Engineering by Lara Ali Sabobeh entitled, “Study of the Impact of Land Use and Land Cover (LULC) Changes on local climate in Six Mega Global Cities in the 21st Century”, submitted in July 2024. Thesis advisor is Dr. Tarig Ali and thesis co-advisor is Dr. Md. Mortula. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Urbanization, characterized by the rapid expansion of city areas and populations, is a significant trend of the 21st century, particularly in developing countries. This thesis investigates the impact of Land Use and Land Cover (LULC) changes on local climates in six major global cities—Beijing, Cairo, Delhi, Istanbul, Lagos, and London—from 2000 to 2023. Utilizing supervised machine learning algorithms, including Classification and Regression Trees (CART), Gradient Tree Boost (GTB), Random Forest (RF), and Support Vector Machine (SVM), Landsat imagery of these global cities was classified, integrating indices like NDVI, NDBI, and MNDWI to enhance LULC classification accuracy. The methodology involved obtaining and preprocessing satellite imagery, applying machine learning algorithms for LULC classification, and analyzing the relationship between LULC changes and local climate represented by land surface temperature (LST) utilizing the GEE platform. Additional data layers like elevation and slope were incorporated to improve model performance, and accuracy was assessed using the following metrics: overall accuracy, kappa statistics, and F-1 scores. The SVM proved to be superior to other algorithms. The findings revealed significant patterns of urban expansion and vegetation loss, highlighting the impact of urbanization on local climates. Urban areas experienced the highest expansions in Istanbul (137.8%) and Lagos (over 100%), while vegetation cover declined in all cities, with Lagos experiencing the highest reduction at 59%. These changes exacerbate urban heat island effects and increase climate-related vulnerabilities. Urbanization raises LSTs, while areas with more vegetation have lower LSTs. The study compared LULC change patterns and socioeconomic indices between developed, developing, and upper-middle-income countries, represented by the selected global cities. London showed a modest increase in developed areas (58.45%) compared to rapid urbanization in Cairo, Lagos, and Delhi. The findings highlight the need for customized urban planning and climate adaptation strategies, with enhanced international cooperation and support for sustainable urbanization initiatives in developing regions being crucial to mitigate the adverse effects of LULC changes on local climate, promoting sustainable urban areas.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
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