Civil & Environmental Engineering Doctoral Work

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Analyses of Highway Project Construction Risks, Performance, and Contingency
(North Dakota State University, 2010) Diab, Mohamed F.
Past studies have highlighted the importance of risk assessment and management in construction projects and transportation industry, and have identified cost and time as the most important risks that transportation professionals want to understand and manage. The main focus of this study is to comprehensively analyze transportation construction risk drivers and identify the correlation of the significant risk drivers with project characteristics, cost growth, schedule growth, and project contingency. This study has adopted 31 relevant and significant programmatic and project-specific risk drivers from different past studies. These risk drivers have been analyzed and evaluated using survey responses from professionals in the context of highway transportation projects. Risk assessments including rating of the encountered risk drivers and their correlation with project characteristics have been carried out within the context of highway construction projects in the United States. Correlations of the construction project performance or risk measures, cost growth percentage, and schedule growth percentage, with the rating values of identified risk drivers values have enabled a better understanding of the impacts of risks and the risk assessment process for highway transportation projects. The impact of significant risk drivers on reported construction cost contingency amounts has also been analyzed. The purpose of this effort was to assess impact of ratings for cost impact, schedule impact, and relative importance of the identified risk drivers on contingency amounts. Predetermined method is the common way to calculate contingency amount in transportation projects. In this study parametric modeling has been used to analyze the relationship between predetermined contingency amounts in transportation projects with perceived risk rating values in order to understand how the expert judgments regarding risk ratings can be used in determination of contingency amounts.
Artificial Intelligence-Empowered Structural Health Monitoring, Damage Diagnosis, and Prognosis of Metallic Structures
(North Dakota State University, 2022) Zhang, Zi
etallic structures are the key backbone of the society and economy, which are often subjected to different types of loadings resulting cracking, corrosion, and other material discontinuity, and affecting structural integrity and safety. Therefore, ultrasonic guided wave (UGW) has been widely used for structural health monitoring (SHM) to gain a deep understanding of structural performance, assess the current state of structural conditions, and avoid potential catastrophic events. Despite advances in technologies and methods in data process, microdamage detection still posts great challenges in their detectability. Different from conventional physics-based methods, artificial intelligence and machine learning (AI/ML) has recently fueled profound automation solutions toward signal process and data fusion, thereby dramatically overcoming the limits. Along this vein, this study aims to propose AI-empowered SHM framework by decoding the UGW to uncover complex interconnected information among data, models, uncertainty, and risk for enhanced structural diagnosis and prognosis to improve metallic structural integrity and safety. Several structural cases, from one-dimensional plates/rods to three-dimensional pipes, were deliberately selected to demonstrate the real-world applications. Three different levels of the AI/ML approaches, from shallow learning to deep learning, are used to explore the effectiveness of the data fusion and data representation. Meanwhile, noise interference and structurally initial nonlinearity as typical structural uncertainty are included in data collection to understand the effects of data quality and uncertainty on the robustness of the proposed methods. The results showed that the proposed method was an efficient and accuracy way to identify the damage characteristics. Results from the shallow learning demonstrated that different features had certain levels of sensitivity to damage, while the feature selection method in the shallow learning revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. However, with the increase of noise level, the shallow learning failed in detectability. By taking advantage of higher automation in feature extraction, the deep learning exhibited significant improvement in accuracy, robustness, and reliability for structural diagnosis and prognosis. Particularly, the higher-layer architecture could outperform the shallow learning in terms of higher effective and efficient data fusion, and enhanced their capability in decoding information over noise interference and structural uncertainty.
Bounding Surface Approach to the Fatigue Modeling of Engineering Materials with Applications to Woven Fabric Composites and Concrete
(North Dakota State University, 2011) Wen, Chao
It has been known that the nucleation and growth of cracks and defects dominate the fatigue damage process in brittle or quasi-brittle materials, such as woven fabric composites and concrete. The behaviors of these materials under multiaxial tensile or compression fatigue loading conditions are quite complex, necessitating a unified approach based on principles of mechanics and thermodynamics that offers good predictive capabilities while maintaining simplicity for robust engineering calculations. A unified approach has been proposed in this dissertation to simulate the change of mechanical properties of the woven fabric composite and steel fiber reinforced concrete under uniaxial and biaxial fatigue loading. The boundary surface theory is used to describe the effect of biaxial fatigue loading. A fourth-order response tensor is used to reflect the high directionality of the damage development, and a second-order response tensor is used to describe the evolution of inelastic deformation due to damage. A direction function is used to capture the strength anisotropic property of the woven fabric composite. The comparisons between model prediction results and experimental data show the good prediction capability of models proposed in this dissertation.
Building Envelope Containing Phase Change Materials for Energy-Efficient Buildings
(North Dakota State University, 2021) Li, Mingli
Energy consumption in the building sector has increased dramatically over the past two decades. The incorporation of phase change materials (PCMs) into building envelopes is considered as effective thermal energy storage to improve building thermal performance and reduce space heating/cooling load. Despite significant efforts in PCMs technologies and their application to buildings, how to select proper PCMs for buildings and maximize the activation of their latent heat to effectively improve building energy efficiency still post great challenges. The lack of systematic and comprehensive studies in these gaps hinders their broad applications in the building sector. This study aims to develop a holistic framework through experimental and numerical studies to gain a deep understanding of the thermal property of PCM and the heat transfer mechanism of the exterior wall integrated with PCM. A novel shape-stabilized paraffin/expanded graphite(EG) composite is prepared and its thermal behavior is investigated through thermal energy storage and heat transfer test. The impact of critical design parameters including the location, thickness, latent heat, melting point, and thermal conductivity of PCM on the thermal performance of a multilayer wall is explored using COMSOL Multiphysics® software. The thermal storage and heat transfer test show that EG can significantly enhance the heat transfer rate of paraffin. In addition, the paraffin/EG composite possesses favorable thermal energy storage ability to decrease the indoor temperature fluctuation and shift the peak load. Among the aforementioned design parameters, melting point of PCM is critical to significantly influence the building thermal performance. To effectively account for melting point of PCM and enhance the service efficiency of PCM, a new wall configuration containing PCM with hybrid melting points is proposed. The proposed wall assembly is found to benefit the indoor thermal comfort and the activation of the latent heat of PCM when the ambient temperature covers cold, mild, and hot loading conditions for the long term. Moreover, coupling vacuum insulation panels (VIP) with extremely low thermal conductivity and PCMs with a large amount of latent heat in the building envelope is another solution to further enhance building thermal performance due to the increased thermal insulation and thermal inertia.
Characterization of Activities of Crumb Rubber in Interaction with Asphalt and its Effect on Final Properties
(North Dakota State University, 2015) Ghavibazoo, Amir
Recycling of millions of scrap tires produced everyday is crucial challenge encountered by waste management systems. Recycling tire rubbers in form of ground tire rubber, known as crumb rubber modifier (CRM), in asphalt industry was introduced in early 1960's and is proved as an effective recycling method. Interaction between CRM and asphalt is physical in nature which happens mainly due to exchange of components between CRM and asphalt and enhances the time temperature dependant properties of asphalt. In this work, the interaction between CRM and asphalt was evaluated through monitoring the evolutions of CRM in asphalt in macro and micro-level. The mechanism and extent of CRM dissolution were monitored under several interaction conditions. The composition of materials released from CRM was investigated using thermo-gravimetric analysis (TGA). The molecular status of the released components were studied using gel permeation chromatography (GPC) analysis. The composition analysis indicated that the CRM start releasing its polymeric components into the asphalt matrix at dissolutions higher than 20%. The released polymeric component of CRM alters the microstructure of the asphalt and creates an internal network at certain interaction temperatures according to viscoelastic analysis. At these temperatures, the released polymeric components are at their highest molecular weight based on GPC results. The effect of released components of CRM on the time temperature dependent properties of asphalt and its glass transition kinetic was monitored using dynamic shear rheometer (DSR) and differential scanning calorimetry (DSC), respectively. The DSC results showed that the intensity of glass transition of the asphalt binder which is mainly defined by the aromatic components in asphalt reduced by absorption of these components by CRM. The evolution of CRM was investigated during short-term aging of the modified asphalt binder. In addition, the effect of presence of CRM and release of its component on oxidization of asphalt binder was evaluated using Fourier transform infrared spectroscopy (FTIR). The results revealed that CRM continue absorbing the aromatic components of asphalt during aging which stiffen the asphalt binder. Also, it was observed that release of oily components of the CRM, which contain antioxidant, reduces oxidization rate of asphalt significantly.
Corrosion Risk Assessment System For Coated Pipeline System
(North Dakota State University, 2018) Deng, Fodan
Steel is widely used as building material for large-scale structures, such as oil and gas pipelines, due to its high strength-to-weight ratio. However, corrosion attack has been long recognized as one of the major reasons of steel pipeline degradation and brings great threat to safety in normal operation of structure. To mitigate the corrosion attacks, coatings are generally applied to protect steel pipelines against corrosion and improve durability of the associated structures for longer service life. Although have higher corrosion resistance, coated pipelines will still get corroded in a long run, as coatings may subject to damages such as cracks. Cracks on coatings could lower the effectiveness of protection for associated structures. Timely updates of up-to-date corrosion rate, corrosion location, and coating conditions to the pipeline risk management model and prompt repairs on these damaged coatings would significantly improve the reliability of protected structures against deterioration and failure. In this study, a corrosion risk analysis system is developed to detect and locate the corrosion induced coating cracks on coated steel using embedded fiber Bragg grating (FBG) sensors. The coatings investigated include high velocity oxygen fuel (HVOF) thermal sprayed Al-Bronze coating, wire arc sprayed Al-Zn coating, and soft coating. Theoretical models of corrosion risk assessment system were carried out followed by systematic laboratory experiments, which shows that the developed system can quantitatively detect corrosion rate, corrosion propagations, and accurately locate the cracks initialized in the coating in real time. This real-time corrosion information can be integrated into pipeline risk management model to optimize the corrosion related risk analysis for resource allocation. To place the sensing units of the system in the most needed locations along the huge pipeline systems for an effective corrosion risk assessment, an example case study is conducted in this study to show how to locate the most critical sensor placement locations along the pipeline using worst case oil and gas discharge analysis. Further applications of the developed system can be integrated with pipeline management system for better maintenance resource allocations.
Crack-Dependent Response of Structural Steel Members Repaired with CFRP
(North Dakota State University, 2014) Hmidan, Amer
Cracking of the lower flange in steel-girder bridges is a critical consideration because it will influence flexural behavior such as load-carrying capacity. Timely rehabilitation will save long-term repair costs and warrant sustainable performance. Carbon-fiber-reinforced polymer (CFRP) is a promising material to repair damaged steel members. This non-metallic reinforcement provides a number of benefits when compared to traditional repair materials (e.g., welded steel plates) for deteriorated steel girders: for example, a favorable strength-to-weight ratio, resistance to corrosion and fatigue, rapid installation in practice, and reduced long-term maintenance expenses. Although applying CFRP to steel members has recently attracted the rehabilitation community, its contribution to the behavior of repaired members is not fully understood. Very limited information about the interaction between the level of initial damage in steel girders and CFRP-repair is available, and also, scant research about the long-term performance and environmental durability for such repaired members has been done. This study addresses these identified research gaps based on a two-phase experimental program. The first phase focuses on CFRP-repaired steel beams having various levels of initial damage (representing multiple stages of fatigue crack propagation). The second phase is focused on testing the repaired beams when subjected to various levels of sustained intensity and cold temperature. A three-dimensional non-linear finite element (FE) model is developed to predict the flexural behavior of CFRP-repaired beams, including CFRP debonding and crack propagation across the critical section of the repaired beams. Also, the FE method is used and regression equations are proposed to predict the static strength of standard steel W Shapes repaired with CFRP, taking into consideration the material and geometric properties.
Development of Improved Methods for Watershed-Scale Topographic Analysis and Hydrologic Modeling
(North Dakota State University, 2020) Wang, Ning
Surface depressions are one of the significant topographic characteristics in depression-dominated areas and can retain runoff and break the hydrologic continuity in watersheds. In traditional semi-distributed models, the entire area of a watershed is assumed to be well connected to its associated outlet and depressions are often lumped as a single depth to control runoff water release. Consequently, hydrologic processes related to depressions cannot be directly simulated. The overall goal of this dissertation research is to analyze and quantify the topographic characteristics of surface depressions and their impacts on hydrologic processes in depression-dominated areas. The specific objectives of this research are: (1) to improve watershed delineation to further reveal the topographic characteristics and hydrologic connectivity within watersheds, (2) to analyze the impact of depressions on runoff processes during rainfall events and the mechanism of water release from depressions, and (3) to analyze the functionalities of depressions in continuous simulation of hydrologic processes and connectivity. A new algorithm was developed for hydrologic unit delineation of depressions and channels (HUD-DC), in which a unique method was proposed to identify depression- and channel-associated hydrologic units and their connections. The HUD-DC delineation results highlighted the significance of depressions and the complex connectivity in depression-dominated areas. Additionally, the delineation under different filling conditions provided helpful guidance for the identification of filling thresholds to remove artifacts in digital elevation models. To achieve the second objective, a depression-oriented, event-based hydrologic model (HYDROL-D) was developed with considering separate modeling for depressional and non-depressional areas, and hierarchical control thresholds for water release from depressions. The HYDROL-D modeling results for a watershed in North Dakota revealed the intrinsic threshold behavior of surface runoff over the watershed and the effectiveness of the hierarchical control thresholds. A depression-oriented hydrologic model with accounting for dynamic hydrologic connectivity (HYDROL-DC) was further developed to continuously track runoff unit by unit. The application of HYDROL-DC in a depression-dominated watershed showed that depressions had not only retention but also acceleration capabilities in surface runoff generation. Additionally, the spatial distribution of depressions exhibited dynamic influences on hydrologic connectivity and the related threshold behavior of runoff processes.
Enhancement of Dispersibility of Zero-Valent Iron Nanoparticles for Environmental Remediation: Entrapment and Surface Modification with Polymers
(North Dakota State University, 2012) Krajangpan, Sita
Nanoscale zero-valent iron (NZVI) particles have been surface modified and used for contaminant remediation. NZVI tend to agglomerate due to magnetic and van der Waals forces and form larger particles that settle down in aqeous media. Agglomerated particles increase in size and have decreased specific surface area and that lead to decrease in their reactivity. In this research, polymer-based surface modifiers were used to increase dispersibility of NZVI for environmental remediation applications. Ca-alginate was selected to entrap NZVI in beads and used to remove aqueous nitrate. The two-way ANOVA test indicates that there was no significant difference between reactivities (towards nitrate) of entrapped NZVI and bare NZVI. While the reactivity of entrapped NZVI was comparable to bare NZVI, the NZVI particles were found to remain agglomerated or clustered together within the alginate beads. A novel amphiphilic polysiloxane graft copolymers (APGC) was designed, synthesized and used to coat NZVI in an attempt to overcome the agglomeration problem. APGC was composed of hydrophobic polysilosin, hydrophilic polyethylene glycol (PEG), and carboxylic acid. The APGC was successfully adsorbed onto the NZVI surfaces via the carboxylic acid anchoring groups and PEG grafts provided dispersibility in water. Coating of NZVI particles with APGC was found to enhance their colloidal stability in water. The APGC possessing the highest concentration of carboxylic acid anchoring group (AA) provided the highest colloidal stability. It was also found that the colloidal stability of the APGC coated NZVI remained effectively unchanged up to 12 months. The sedimentation characteristics of APGC coated NZVI (CNZVI) under different ionic strength conditions (0-10 mM NaCl and CaCl2) did not change significantly. Degradation studies were conducted with trichloroethylene (TCE) and arsenic(V) [As(V)] as the model contaminants. TCE degradation rates with CNZVI were determined to be higher as compared to bare NZVI. Shelf-life studies indicated no change on TCE degradation by CNZVI over a 6-month period. As(V) removal batch studies with CNZVI were conducted to in both aerobic and anaerobic conditions. Increase in arsenic removal efficiency was observed with CNZVI as compare to bare NZVI in both aerobic and anaerobic conditions. Ionic strengths showed minimal inhibiting effect on arsenic removal by CNZVI.
Estimation of the Capacity of a Basic Freeway and Weaving Segment Under Traditional, Autonomous, and Connected Autonomous Vehicles, Using Oversaturated Traffic Condition Data
(North Dakota State University, 2022) Saha, Niloy
Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) will be the standard in transportation in the future. The use of such vehicles could minimize traffic oscillation and travel time and boost safety and mobility on freeways. An AV is a self-driving vehicle that can make decisions by itself in any situation. CAVs include all the characteristics of AVs and additional communication with other vehicles or the infrastructure (signal system). The use of AVs and CAVs will substantially increase motorway capacity in upcoming decades. Moreover, vehicle dynamics will change as technology and algorithms become more commonplace. In the short term, capacity may have a negative impact on talent; however, as the algorithms become more aggressive, the results will improve. Highway Capacity Manual (HCM) may need to be updated if freeway system capacity changes. As a result, the manual should focus on enhancing two freeway segments: the fundamental freeway portion and the weaving part (case study on U.S. 101 in Los Angeles, California). A microsimulation program developed by the Planung Transport Verkehr (PTV) in Karlsruhe, Germany, was used to calibrate and evaluate Wiedemann's behavioral car-following model (CFM). The Coexist project from Europe created three types of autonomous cars: AV-cautious, AV-normal, and AV all-knowing. CFMs are vital because they measure the distance between vehicles. This is crucial for capacity. The capacity of AV cautious vehicles is decreased at all levels and penetrations. When AV-cautious autonomy evolves into AV all-knowing autonomy, the capacity of the weaving section and the BFS may rise by 33% and 36%, respectively. This study provides a method for evaluating the capacity of freeways, which we estimate using AV levels and penetrations. Transportation planners and traffic engineers may utilize these capabilities to design better traffic planning and traffic-management technology in the future. For example, highway capacity will be restricted if the AV mix is largely AV-cautious. However, the solution is likely not to expand capacity but to find ways to manage traffic as new technology develops and moves to CAVs. This research aids in the planning and design of how to bring AVs and CAVs to market.
Fate and Characteristics of Dissolved Organic Nitrogen through Wastewater Treatment Systems
(North Dakota State University, 2012) Simsek, Halis
Dissolved organic nitrogen (DON) represents a significant portion (25-80%) of total dissolved nitrogen in the final effluent of wastewater treatment plants (WWTPs). DON in treated wastewater, once degraded, causes oxygen depletion and/or eutrophication in receiving waters and should be reduced prior to discharge. Biodegradability, bioavailability, and photodegradability are important characteristics of wastewater derived DON and are subjects of research in this dissertation. Four research tasks were performed. In the first task, laboratory-scale chemostat experiments were conducted to examine whether solids retention time (SRT) could be used to control DON and biodegradable DON (BDON) in treated wastewater. Nine different SRTs from 0.3 to 13 were studied. There was no correlation between effluent DON and SRTs. However, BDONs at SRTs of 0.3 to 4 days were comparable and had a decreasing trend with SRTs after that. These results indicate the benefit of high SRTs in term of producing effluent with less BDON. The second task was a comprehensive year-round data collection to study the fate of DON and BDON through the treatment train of a trickling filter (TF) WWTP. The plant removed substantial amounts of DON (62%) and BDON (76%) mainly through the biological process. However, the discharged concentrations in the effluent were still high enough to be critical for a stringent total nitrogen discharge limit (below 5 mg-N/L). Evolution of bioavailable DON (ABDON) along the treatment trains of activated sludge (AS) and TF WWTPs and relationship between ABDON and BDON were examined in the third task. ABDON exerted from a combination of bacteria and algae inocula was higher than algae inoculated ABDON and bacteria inoculated BDON suggesting the use of algae as a treatment organism along with bacteria to minimize effluent DON. The TF and AS WWTPs removed 88% and 64% of ABDON, respectively. In the last task, photodegradable DON (PDON) in primary wastewater and final effluent from TF and AS WWTPs was studied. PDON and BDON fractions of DON data in the final effluent of TF and AS WWTP samples elucidate that photodegradation is as critically important as biodegradation when mineralization of effluent DON is a concern in receiving waters.
Fate and Transformation of a Conjugated Natural Hormone 17β-Estradiol-3-Glucuronide in Soil-Water Systems
(North Dakota State University, 2011) Shrestha, Suman Lal
The objectives of the study were to investigate the sorption and degradation of a glucuronide conjugated natural hormone, 17β-estradio1-3-glucuronide (E2-3G), and its estrogenic metabolites in soil-water systems. Radiolabeled E2-3G was first synthesized in the laboratory. Soil-water batch experiments were then conducted using natural and sterilized topsoil (0-6 cm) and subsoil (18-24 cm) with the radiolabeled E2-3G to investigate the effects of soil organic matter content and microbial activity. The aqueous dissipation of 14C in the batch experiments followed a biphasic pattern with an initial rapid dissipation phase followed by a second slower phase. Significant differences in total aqueous 14C dissipation were observed for the different initial concentrations for both soils, with greater persistence of intact E2-3G at higher initial concentrations.
Hygrothermal Effects of Air Cavities Behind Claddings on Building Envelopes
(North Dakota State University, 2022) Xie, Yanmei
Air cavity behind claddings within building envelope provides an approach to mitigating building moisture-related issues as well as improving the building’s thermal performance. However, studies in literature commonly assume the cavity air as still and thus neglect the influence of mixed convection on the performance of building envelope. In addition, the drying performance of the air cavities remains unknown, and commonly a rectangular unicellular cavity is improperly assumed to simplify the investigation of the hygrothermal performance of a cladding system. Moreover, the literature lacks a study of the effect of humid air in the air cavity on heat and mass transfer. Therefore, it necessitates advanced problem formulation and solving to comprehensively study the effects of air cavities behind claddings on the performance of building envelope. The specific objectives are to 1) investigate potential of self-drying siding with raised air cavities for building envelopes; 2) study the effects of the cavity depth in mixed convection of air cavity for building envelopes; 3) analyze the effects of humid air in an air cavity on mass and heat transfer with phase change at the wall. To achieve these objectives, firstly, this study redefines the drying potential of air cavity taking into account the air cavity depth related to the shape irregularity and the inlet and outlet uncertainties. Then the formulated problems of mixed convection of air cavities behind sidings are solved with a perturbation method and SIMPLER algorithm. The results show that the drying performance is found to be heavily dependent on the cavity depth. Further, increasing the ratio of the siding depth to the air cavity depth amplifies the cavity air’s velocity, temperature, and mass fraction at cavity walls, as well as the heat and mass transfer across cavities. Consequently, this study demonstrated that humid air with the phase change and the cavity depth have the significant effects on the hygrothermal performance of building envelopes. The outcome of this study provides valuable guidance on the thermal performance evaluation of air cavity and has the potential of improving the design of claddings for the overall hygrothermal performance of building envelope.
Identification, Categorization, and Prediction of Drought in Cold Climate Regions
(North Dakota State University, 2021) Bazrkar, Mohammad Hadi
To mitigate drought losses, identification, categorization, and prediction of droughts are essential. The objectives of this dissertation research are (1) to improve drought identification in cold climate regions by developing a new hydroclimatic aggregate drought index (HADI) and a snow-based hydroclimatic aggregate drought index (SHADI), (2) to customize drought categorization by considering both spatial and temporal distributions of droughts, and (3) to improve drought prediction by modifying the traditional support vector regression (SVR). R-mode principal component analyses (PCA) are conducted for rainfall, snowmelt, surface runoff, and soil water storage to derive the HADI. Instead of rainfall and snowmelt in the HADI, precipitation and snowpack are used to estimate the SHADI for adding the capability of snow drought identification. Drought frequencies and classes form a bivariate distribution function by applying a joint probability distribution function. A conditional expectation is further used to estimate the probability of occurrence of droughts. To derive variable threshold levels for drought categorization, hierarchical K-means clustering is used. For drought prediction, a change point detection method is employed to split the non-stationary time series into multiple stationary time series. SVR is further performed on each stationary time series to predict drought. The new drought methods were applied to the Red River of the North Basin (RRB). The 1979-2010 and 2011-2016 data obtained from the North American land data assimilation system were used for training and testing, respectively. Precipitation, temperature, and evapotranspiration were selected as the predictors, and the target variables consisted of multivariate HADI and SHADI, bivariate standardized drought indices, and univariate standardized drought indices. The results showed that the new HADI and SHADI, together with the customized drought categorization, were able to provide more accurate drought identification and characterization, especially for cold climate regions. The comparison of the results of the traditional and modified SVR models in the RRB demonstrated better performance of the modified SVR, particularly when drought indices with higher sensitivity to temperature were used. The methodologies developed in this dissertation research can be used for improving drought identification, categorization, and prediction, as well as further mitigating the potential adverse impacts of droughts.
Image-Based Hybrid Structural Health Monitoring Through Artificial Intelligence
(North Dakota State University, 2022) Bai, Xin
Bridges are widely used in human life. Understanding structural performance, assessing structural conditions, and providing in-time decision are crucial components in structural health monitoring (SHM), to avoid catastrophic events and improve public safety. However, traditional SHM needs traffic closure, extensive sensor deployment, and in-contact measurements. The main purpose of this thesis is to develop a vision-based sensor of high accuracy that does not need artificial targets. When the vibration of the UAV itself is removed, the UAV is a convenient method to record video of the vibrations. Based on the recorded images and vibration data, a new deep learning method is developed and used to analyze vibrations of the structure and detect damage locations and conditions automatically. In the thesis, a non-contact vision sensor system for monitoring structural displacements with an advanced Zernike subpixel edge detection technique is first suggested. A new method to filter the effect of camera motions through background templates is proposed in the study. Several experiments on the MTS machine were performed with different frequencies and amplitudes to verify the method. The results show that filtering of vibrations of the camera significantly improves the displacement monitoring accuracy from 53.0% to 97.0%. Three translations and three rotations of the unmanned aerial vehicle (UAV) were derived through the suggested fast Normalized Cross Correlation (NCC) based template matching method, and their effect on the monitored structural displacement is analyzed. To verify the concept, a series of lab and field experiments were performed. Excellent precision and consistency were obtained for the UAV monitored displacement, the MTS piston motion, and the fixed camera derived displacement. Further in the thesis, a novel deep learning-based structural health monitoring method was developed, which could detect damages using both defects and vibration data. Two ABAQUS models on a beam and an ABAQUS model on a truss were conducted to test if the proposed CNN model could detect damages successfully. Seven transfer learning methods were compared on detecting crack images. From the outputs of the deep learning models, it is apparent that the AlexNet CNN model with defect images shows higher accuracy in estimating damage status.
Improved Hydrologic Modeling for Characterizing Variable Contributing Areas and Threshold-Controlled Overland Flow in Depression-Dominated Areas
(North Dakota State University, 2020) Zeng, Lan
Surface depressions are important topographic features, which affect overland flow, infiltration, and other hydrologic processes. Specifically, depressions undergo filling-spilling-merging-splitting processes under natural rainfall conditions, featuring discontinuity in hydrologic connectivity and variability in contributing area. However, a constant and time-invariant contributing area is often assumed in traditional hydrologic modeling, and consequently, the real threshold-controlled overland flow dynamics cannot be captured. The overall goal of this dissertation research is to improve hydrologic modeling, especially for depression-dominated areas, by quantifying the hydrologic effects of depressions. The specific objectives are to analyze the hydrotopographic characteristics of depressions and identify the intrinsic relationships of hydrologic variables, develop new modeling methods to simulate the depression-oriented dynamics in overland flow and variations in contributing area, and reveal the influence of spatially distributed depressions on the surface runoff generation and propagation processes. To achieve these objectives, three studies were conducted: (1) the frequency distribution of depression storage capacities was determined and a puddle-based unit (PBU)-probability distribution model (PDM) was developed; (2) the intrinsic changing patterns of contributing area and depression storage were identified, based on which a new depression-oriented variable contributing area (D-VCA) model was developed; and (3) a modified D-VCA (MD-VCA) model was further developed by introducing a depressional time-area zone scheme and a new variable contributing area-based surface runoff routing technique to account for the spatial distribution of depressions. These three models (PBU-PDM, D-VCA, and MD-VCA) were evaluated through the applications to depression-dominated watersheds in North Dakota, and simulation results demonstrated their capabilities in simulating the variations of contributing areas and threshold-controlled overland flow dynamics. In addition, these three studies emphasized the important roles of depressions in the evolution of contributing areas as well as surface runoff generation and propagation. Without considering the spatial distribution of depressions, the formation of contributing area and the timing and quantity of runoff contributions cannot be characterized.
In Situ Groundwater Remediation using Enricher Reactor-Permeable Reactive Biobarrier
(North Dakota State University, 2012) Somayajula, Sreerama Murthy Kasi
Permeable reactive biobarrier (PRBB) is a flow-through zone where microorganisms degrade contaminants in groundwater. Discontinuous presence of contaminants in groundwater causes performance loss of a PRBB in removing the target contaminant. A novel enricher reactor (ER) - PRBB system was developed to treat groundwater with contaminants that reappear after an absence period. ER is an offline reactor for enriching contaminant degraders, which were used for augmenting PRBB to maintain its performance after a period of contaminant absence. The ER-PRBB concept was initially applied to remove benzene that reappeared after absence periods of 10 and 25 days. PRBBs without ER augmentation experienced performance losses of up to 15% higher than ER-PRBBs. The role of inducer compounds in the ER to enrich bacteria that can degrade a mixture of benzene, toluene, ethylbenzene, and xylene (BTEX) was investigated with an objective to minimize the use of toxic chemicals as inducers. Three inducer types were studied: individual BTEX compounds, BTEX mixture, and benzoate (a non toxic and a common intermediate for BTEX biodegradation). Complete BTEX removal was observed for degraders enriched on all three inducer types; however, the removal rates were dependent on the inducer type. Degraders enriched on toluene and BTEX had the highest degradation rates for BTEX of 0.006 to 0.014 day-1 and 0.006 to 0.012 day-1, respectively, while degraders enriched on benzoate showed the lowest degradation rates of 0.004 to 0.009 day-1. The ER-PRBB technique was finally applied to address the performance loss of a PRBB due to inhibition interactions among BTEX, when the mixture reappeared after a 10 day absence period. The ER-PRBBs experienced minimal to no performance loss, while PRBBs without ER augmentation experienced performance losses between 11% and 35%. Presence of ethanol during the BTEX absence period increased the performance loss of PRBB for benzene removal. PRBBs augmented with degraders enriched on toluene alone overcame the inhibition interaction between benzene and toluene indicating that toluene can be used as a single effective inducer in an ER. The ER-PRBB was demonstrated to be a promising remediation technique and has potential for applications to a wide range of organic contaminants.
An Integrated System for Road Condition and Weigh-in-Motion Measurements using In-Pavement Strain Sensors
(North Dakota State University, 2016) Zhang, Zhiming
The United States has the world’s largest road network with over 4.1 million miles of roads supporting more than 260 million of registered automobiles including around 11 million of heavy trucks. Such a large road network challenges the road and traffic management systems such as condition assessment and traffic monitoring. To assess the road conditions and track the traffic, currently, multiple facilities are required simultaneously. For instance, vehicle-based image techniques are available for pavements’ mechanical behavior detection such as cracks, high-speed vehicle-based profilers are used upon request for the road ride quality evaluation, and inductive loops or strain sensors are deployed inside pavements for traffic data collection. Having multiple facilities and systems for the road conditions and traffic information monitoring raises the cost for the assessment and complicates the process. In this study, an integrated system is developed to simultaneously monitor the road condition and traffic using in-pavement strain-based sensors, which will phenomenally simplify the road condition and traffic monitoring. To accomplish such a superior system, this dissertation designs an innovative integrated sensing system, installs the integrated system in Minnesota's Cold Weather Road Research Facility (MnROAD), monitors the early health conditions of the pavements and ride quality evaluation, investigates algorithms by using the developed system for traffic data collection especially weigh-in-motion measurements, and optimizes the system through optimal system design. The developed integrated system is promising to use one system for multiple purposes, which gains a considerable efficiency increase as well as a potential significant cost reduction for intelligent transportation system.
Investigating Biodegradability of Dissolved Organic Nitrogen in Oligotrophic and Eutrophic Systems
(North Dakota State University, 2014) Wadhawan, Tanush
Dissolved organic nitrogen (DON) in water and wastewater is a major public concern. In drinking water treatment plants (WTP), DON and biodegradable DON (BDON) may form carcinogenic by-products during disinfection and might also serve as a nutrient for microbiological growth in distribution systems. BDON in treated wastewater can promote algal growth in receiving water bodies. Understanding biodegradability of DON is important to develop strategies and processes capable of minimizing DON impact on the wastewater effluent receiving water bodies and drinking water. WTPs are nutrient-poor oligotrophic systems that receive source water with DON of about ≤2 mg N/L. Wastewater treatment plants (WWTPs) are nutrient-rich eutrophic systems which receive raw wastewater with DON of ≥8 mg N/L. At WWTPs, sidestream deammonification is a highly eutrophic system employed to treat highly concentrated streams of DON (≥100 mg N/L) and ammonia (≥1,500 mg N/L) generated from filtrate from anaerobically digested sludge dewatering. DON characteristics including biodegradability for different trophic levels could differ. The main goal of this dissertation is to investigate biodegradability of DON in these oligotrophic and eutrophic systems. Three research tasks were performed. In the first task, a method to measure BDON in oligotrophic systems was developed and applied to determine the fate of BDON along four treatment stages of a WTP with ozonation prior to filtration. Optimum dose of inocula and incubation time were identified for the BDON measurement. The Moorhead WTP, Moorhead, MN on average removed 30% of DON and 68% of BDON. The second task involved investigating the role of four biological wastewater treatment processes in removing DON from eutrophic systems. Nitrification process biodegraded 70, 54, and 57% of DON in influent, primary effluent, and secondary effluent, respectively. Heterotrophic DON removal was less (1.7 to 38%) while denitrification and deammonification did not remove DON. For the third task, BDON biodegradability in highly eutrophic system was investigated using nitrifying sludge. About 45 to 90% of DON in sidestream effluent was biodegradable. Information from this dissertation provides a better understanding on DON and BDON fate through water and wastewater treatment processes representing different trophic levels.
Neural Networks and Sensitivity Analysis for Detection and Interpretation of Structural Damage
(North Dakota State University, 2021) Lavadiya, Dayakar Naik
Computer vision (CV)-based approaches have gained a lot of attention in recent years for objective identification of damages both at structural and material scales. In this dissertation, the metallurgical phases and the two important modes of damage in structural steel, namely fracture and corrosion, are considered. Use of CV techniques for metallurgical phase identification and fracture type identification in steel microstructure is minimal and rely on pixel intensity information. When distinct phases or fracture types possess similar pixel intensities, predictions may be erroneous. In this dissertation, various texture recognition algorithms based on an ensemble of machine learning algorithms are proposed to identify the distinct metallurgical phases and fracture types in structural steels. The existing CV-based corrosion detection techniques are efficient for the images acquired under natural daylight illumination and ignore the inherent variations in ambient lighting conditions. Further, corrosion-like hues such as bricks, surrounding vegetation, etc., present in the images yields corrosion misclassification. Furthermore, there are currently no techniques available to identify the source of corrosion (HCl, NaCl, and Na2SO4). In this dissertation, various color spaces are employed in conjunction with neural networks to identify the corrosion in real-world scenarios such as varying natural daylight illuminations, shadows, water wetting, and oil wetting. For eliminating the visual ambiguity and identifying the source of corrosion, the visible and near-infrared (VNIR) spectra are extracted to train support vector machines. Deep neural networks (DNN’s) popularly used in the field of CV are often regarded as the black box models. Sensitivity analysis (SA) is a model-agnostic explainable artificial intelligence (XAI) approach commonly employed to explain the outcome of a mathematical model. SA quantifies the variation in the model's output to the change in the input of the model. In this dissertation, a novel sensitivity analysis referred to as Complex-Step Sensitivity Analysis is developed for interpreting the DNN’s prediction. Numerical experiments are performed to demonstrate the efficacy of the proposed method in evaluating the derivatives of DNN predictions and identifying the important features. Using this newly developed method, the key wavelengths in the VNIR spectra contributing to the prediction of corrosion source corrosion are identified.

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