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- Michigan Tech Publications (4)
- Civil, Architectural and Environmental Engineering Faculty Research & Creative Works (3)
- Civil & Environmental Engineering Faculty Publications (2)
- Civil Engineering Faculty Publications and Presentations (2)
- Civil and Environmental Engineering Faculty Publications (2)
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- Civil Engineering Faculty Publications (1)
- Civil and Environmental Engineering and Construction Faculty Research (1)
- Department of Biological Systems Engineering: Papers and Publications (1)
- Department of Statistics: Faculty Publications (1)
- Electrical & Computer Engineering Faculty Publications (1)
- Electrical and Computer Engineering Publications (1)
- Engineering Management & Systems Engineering Faculty Publications (1)
- Publications (1)
- Research outputs 2022 to 2026 (1)
Articles 1 - 22 of 22
Full-Text Articles in Engineering
Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall
Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall
Civil & Environmental Engineering Faculty Publications
This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …
Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park
Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park
Engineering Management & Systems Engineering Faculty Publications
Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …
Statistical And Deep Learning Models For Reference Evapotranspiration Time Series Forecasting: A Comparison Of Accuracy, Complexity, And Data Efficiency, Arman Ahmadi, Andre Daccache, Mojtaba Sadegh, Richard L. Snyder
Statistical And Deep Learning Models For Reference Evapotranspiration Time Series Forecasting: A Comparison Of Accuracy, Complexity, And Data Efficiency, Arman Ahmadi, Andre Daccache, Mojtaba Sadegh, Richard L. Snyder
Civil Engineering Faculty Publications and Presentations
Reference evapotranspiration (ETo) is an essential variable in agricultural water resources management and irrigation scheduling. An accurate and reliable forecast of ETo facilitates effective decision-making in agriculture. Although numerous studies assessed various methodologies for ETo forecasting, an in-depth multi-dimensional analysis evaluating different aspects of these methodologies is missing. This study systematically evaluates the complexity, computational cost, data efficiency, and accuracy of ten models that have been used or could potentially be used for ETo forecasting. These models range from well-known statistical forecasting models like seasonal autoregressive integrated moving average (SARIMA) to state-of-the-art deep learning (DL) algorithms like temporal fusion transformer …
Prediction Of Drilling Fluid Lost-Circulation Zone Based On Deep Learning, Yili Kang, Chenglin Ma, Chengyuan Xu, Lijun You, Zhenjiang You
Prediction Of Drilling Fluid Lost-Circulation Zone Based On Deep Learning, Yili Kang, Chenglin Ma, Chengyuan Xu, Lijun You, Zhenjiang You
Research outputs 2022 to 2026
Lost circulation has become a crucial technical problem that restricts the quality and efficiency improvement of the drilling operation in deep oil and gas wells. The lost-circulation zone prediction has always been a hot and difficult research topic on the prevention and control of lost circulation. This study applied machine learning and statistical methods to deeply mine 105 groups and 29 features of loss data from typical loss block M. After removing 10 sets of noise data, the methods of mean removal, range scaling and normalization were used to pre-treat the 95 sets of the loss data. The multi-factor analysis …
Tempnet – Temporal Super-Resolution Of Radar Rainfall Products With Residual Cnns, Muhammed Ali Sit, Bongchul Seo, Ibrahim Demir
Tempnet – Temporal Super-Resolution Of Radar Rainfall Products With Residual Cnns, Muhammed Ali Sit, Bongchul Seo, Ibrahim Demir
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the …
Hardware-In-The-Loop And Digital Twin Enabled Autonomous Robotics-Assisted Environment Inspection, Johnny Li, Bo Shang, Iresh Jayawardana, Genda Chen
Hardware-In-The-Loop And Digital Twin Enabled Autonomous Robotics-Assisted Environment Inspection, Johnny Li, Bo Shang, Iresh Jayawardana, Genda Chen
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Empowered by the advanced 3D sensing, computer vision and AI algorithm, autonomous robotics provide an unprecedented possibility for close-up infrastructure environment inspection in an efficient and reliable fashion. Deep neural network (DNN) learning algorithms, pretrained on the large database can empower real-time object detection as well as fully autonomous, safe robotic navigation in unstructured environments while avoiding the potential obstacle. However, the development and deployment of the robots, inspection planning and operation procedures are still tedious and segmented with tremendous manual intervention during environmental inspection and anomaly monitoring. The proposed digital twin approach is able to provide a virtual representation …
Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen
Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the …
Electrochemical Biosensing And Deep Learning-Based Approaches In The Diagnosis Of Covid-19: A Review, Omer Sadak, Ferhat Sadak, Ozal Yildirim, Nicole M. Iverson, Rizwan Qureshi, Muhammed Talo, Chui Ping Ooi, U. Rajendra Acharya, Sundaram Gunasekaran, Tanvir Alam
Electrochemical Biosensing And Deep Learning-Based Approaches In The Diagnosis Of Covid-19: A Review, Omer Sadak, Ferhat Sadak, Ozal Yildirim, Nicole M. Iverson, Rizwan Qureshi, Muhammed Talo, Chui Ping Ooi, U. Rajendra Acharya, Sundaram Gunasekaran, Tanvir Alam
Department of Biological Systems Engineering: Papers and Publications
COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The "gold standard'' for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns …
Coevolution Of Machine Learning And Process-Based Modelling To Revolutionize Earth And Environmental Sciences: A Perspective, Mojtaba Sadegh
Coevolution Of Machine Learning And Process-Based Modelling To Revolutionize Earth And Environmental Sciences: A Perspective, Mojtaba Sadegh
Civil Engineering Faculty Publications and Presentations
Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Michigan Tech Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Long Short-Term Memory Based Subsurface Drainage Control For Rainfall-Induced Landslide Prevention, Aynaz Biniyaz, Behnam Azmoon, Ye Sun, Zhen Liu
Long Short-Term Memory Based Subsurface Drainage Control For Rainfall-Induced Landslide Prevention, Aynaz Biniyaz, Behnam Azmoon, Ye Sun, Zhen Liu
Michigan Tech Publications
Subsurface drainage has been widely accepted to mitigate the hazard of landslides in areas prone to flooding. Specifically, the use of drainage wells with pumping systems has been recognized as an effective short-term solution to lower the groundwater table. However, this method has not been well considered for long-term purposes due to potentially high labor costs. This study aims to investigate the idea of an autonomous pumping system for subsurface drainage by leveraging con-ventional geotechnical engineering solutions and a deep learning technique—Long-Short Term Memory (LSTM)—to establish a geotechnical cyber-physical system for rainfall-induced landslide prevention. For this purpose, a typical soil …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Civil & Environmental Engineering Faculty Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Research And Applications Of Artificial Neural Network In Pavement Engineering: A State-Of-The-Art Review, Xu Yang, Jinchao Guan, Ling Ding, Zhanping You, Vincent C.S. Lee, Mohd Rosli Mohd Hasan, Xiaoyun Cheng
Research And Applications Of Artificial Neural Network In Pavement Engineering: A State-Of-The-Art Review, Xu Yang, Jinchao Guan, Ling Ding, Zhanping You, Vincent C.S. Lee, Mohd Rosli Mohd Hasan, Xiaoyun Cheng
Michigan Tech Publications
Given the great advancements in soft computing and data science, artificial neural network (ANN) has been explored and applied to handle complicated problems in the field of pavement engineering. This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering, including pavement design, construction, inspection and monitoring, and maintenance. This study focused on the papers published over the last three decades, especially the studies conducted since 2013. Through literature retrieval, a total of 683 papers in this field were identified, among which 143 papers were selected for an in-depth review. The …
Efficient, Low-Cost Bridge Cracking Detection And Quantification Using Deep-Learning And Uav Images, Chao Sun, Xiangyu Meng, Joshua O. Ogbebor, Shaopan Guo
Efficient, Low-Cost Bridge Cracking Detection And Quantification Using Deep-Learning And Uav Images, Chao Sun, Xiangyu Meng, Joshua O. Ogbebor, Shaopan Guo
Publications
Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This …
Evaluation Of Deep Learning Against Conventional Limit Equilibrium Methods For Slope Stability Analysis, Behnam Azmoon, Aynaz Biniyaz, Zhen (Leo) Liu
Evaluation Of Deep Learning Against Conventional Limit Equilibrium Methods For Slope Stability Analysis, Behnam Azmoon, Aynaz Biniyaz, Zhen (Leo) Liu
Michigan Tech Publications
This paper presents a comparison study between methods of deep learning as a new cat-egory of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to cal-culate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was ver-ified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive …
A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz
A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz
Electrical and Computer Engineering Publications
With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent …
Evaluation Of Bridge Decks With Overlays Using Impact Echo, A Deep Learning Approach, Sattar Dorafshan, Hoda Azari
Evaluation Of Bridge Decks With Overlays Using Impact Echo, A Deep Learning Approach, Sattar Dorafshan, Hoda Azari
Civil Engineering Faculty Publications
In this paper, the feasibility of using deep learning models (DLMs) for evaluation of bridges with overlay systems is investigated. Several laboratory-made concrete specimens with artificial subsurface defects and overlay systems (bonded and debonded) made of cement and asphalt overlay materials were tested using impact echo (IE). One-dimensional (1D) and two-dimensional (2D) convolutional neural networks (CNNs) were developed, trained, and tested on the IE data. The proposed 1D CNN was the most successful in detecting debonding and subsurface defects; it achieved an average accuracy of 0.68 on the cement overlay specimens and 0.58 for asphalt overlay specimens. Maps of the …
Multi-Level-Phase Deep Learning Using Divide-And-Conquer For Scaffolding Safety, Sayan Sakhakarmi, Jee Woong Park
Multi-Level-Phase Deep Learning Using Divide-And-Conquer For Scaffolding Safety, Sayan Sakhakarmi, Jee Woong Park
Civil and Environmental Engineering and Construction Faculty Research
A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety prediction for scaffolds. It implements a divide-and-conquer technique with deep learning. As a test scaffolding, a four-bay, three-story scaffold model was used. Analysis of the model led to 1411 unique safety cases for the model. To apply deep learning, a test simulation generated 1,540,000 datasets for pre-training, and an additional 141,100 datasets for testing purposes. The cases were then sub-divided into 18 categories …
Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh
Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh
Department of Statistics: Faculty Publications
Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …
Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos
Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos
Electrical & Computer Engineering Faculty Publications
Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …
Sdnet2018: An Annotated Image Dataset For Non-Contact Concrete Crack Detection Using Deep Convolutional Neural Networks, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Sdnet2018: An Annotated Image Dataset For Non-Contact Concrete Crack Detection Using Deep Convolutional Neural Networks, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Civil and Environmental Engineering Faculty Publications
SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field …
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Civil and Environmental Engineering Faculty Publications
This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they …