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.
Bonding Performances of Epoxy-Based Composites Reinforced by Carbon Nanotubes
(North Dakota State University, 2022) Zhang, Dawei
Epoxy resin has been exclusively used in many civil engineering applications such as adhesive joints and anti-corrosive coatings, but most of the usages of epoxy resin highly rely on a solid adhesive bonding between the epoxy matrix and the substrate material. In order to improve the bonding performance of epoxy resin, carbon nanotubes (CNTs) are incorporated into the epoxy resin due to their extraordinary mechanical properties. Although CNTs are expected to be promising additives for epoxy resin, the reinforcing efficiency of CNTs is still far from satisfactory, the bonding performance of CNT reinforced epoxy composites remains an essential research issue. In this dissertation, a systematic study was carried out to investigate the bonding performances of epoxy-based composites reinforced using CNTs. The influences of two main influential parameters (surface roughness and bondline thickness) on the bonding performance of epoxy-based composites were examined. It was found that rougher steel substrates or thinner epoxy bondlines yielded better bonding performances for both unreinforced and CNT reinforced epoxy composites. However, according to the SEM image analyses, the reinforcing efficiency of CNTs was restricted by the non-uniform dispersion of CNTs in the epoxy matrix resulted from CNT agglomeration and entanglement. Given that the great variances of CNT geometries may inevitably result in extensive differences on CNT dispersion status and reinforcing efficiencies in CNT reinforced epoxy composites, the dispersion characterizations and bonding performance of CNT reinforced epoxy composites with different CNT geometries were studied. The experimental results indicated that CNTs with larger diameter (50-100 nm) had a greater ability to achieve more uniform dispersion which further led to better bonding performance. Although CNT length did not have an evident effect on the CNT dispersion, epoxy-based composites reinforced by normal-length CNTs (5-20 μm) had higher bonding strength and toughness than those by shorter CNTs (0.5-2 μm). To further improve the dispersion effectiveness of CNTs, a novel CNT mixing method using carboxymethyl cellulose (CMC) was proposed. It was proved that better CNT dispersion resulted from the CMC surface treatment significantly improved the bonding performance of CNT reinforced epoxy composites.
Multiscale Modeling of Conjugated Polymers Towards Predicting Their Multifunctional Behaviors
(North Dakota State University, 2022) Alesadi, Amirhadi
Easy processability, tunable mechanical and optoelectronic properties, and the endless possibilities of molecular modifications enable conjugated polymers (CPs) to be used in a wide range of lightweight, low-cost, and flexible organic electronic devices. However, despite the tremendous efforts in molecular engineering and improved electronic properties, the thermomechanical-structure-property relationship of these semiconducting materials is still less investigated. Predicting the thermal, mechanical, and photovoltaics (PV) properties of CPs is challenging due to heterogeneous chain architectures and diverse chemical building blocks. To address this critical issue, this dissertation aims to employ novel multiscale modeling and data-driven approaches to characterize the thermomechanical and optoelectronic properties of CPs. In particular, we utilize machine learning (ML) and scale-bridging molecular modeling techniques scaled from quantum mechanics simulations to force field all atomistic molecular dynamics (AA-MD) and coarse-grained (CG)-MD simulations to explore the multifunctional behavior of CPs. Validated by experimental measurements, our AA-MD and CG-MD simulations can capture glass transition temperature (Tg), elastic modulus, and strain-induced deformation mechanism of the CPs with varied side-chain lengths and backbone moieties. Furthermore, through the integration of ML, AA-MD simulations, and experiments, we propose a simplified but accurate predictive framework to quantify Tg directly from the geometry of the CPs’ repeat unit. Next, first-principle calculations developed upon density functional theory (DFT) are utilized to quantify electronic configuration changes of CPs due to the charge injection. Finally, created upon ab initio excited state dynamics, we report a computational methodology to explore the PV performance of donor-acceptor organic bulk heterojunctions (BHJ). This computational framework facilitates screening of the best donor-acceptor molecules and narrows down the list of potential candidates to be used in BHJ. We believe data-driven and multiscale modeling approaches established in this dissertation are important milestones for the design and structure-property prediction of CPs and organic BHJ, paving the way for developing the next generation of organic electronics.
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.
Optimizing Selection and Implementation Protocols of Lean Construction Tools and Techniques for Rapid Initial Successes
(North Dakota State University, 2021) Aslam, Mughees
Lean construction (LC) has been considered as one of the most promising project management philosophies to overcome low productivity and excessive waste issues impacting the construction industry. Despite strong philosophies and some successful implementations, the uptake of LC in the construction industry is very low due to convoluted implementing strategies. Specifically, the construction industry lacks effective evaluation criteria, selection framework, and integrated applications of LC principles, tools, and techniques. Moreover, there is a strong need for a practical framework and associated validation process for LC implementation. Therefore, the purpose of this research is to optimize the selection and implementation protocols of LC tools and techniques for rapid initial successes. The methodology used for this research includes (1) a systematic literature review (SLR), (2) an initial survey of LC practitioners, (3) development of selection and implementation frameworks, and (4) framework validation survey and analysis. Uniquely, interpretative structural modeling (ISM) was used to develop the initial LC implementation framework and structural equation modeling (SEM) was used for framework modification and validation. As a result of the study, an effective selection framework has been developed with recommended LC tools and techniques to achieve integrated LC. The study has also identified critical factors for rapid initial LC project success and developed a robust LC implementation framework and an innovative integrated Last Planner System (ILPS). The validated LC implementation framework can predict approximately 65% of the variance in the project outcomes based on eight performance outcome measures. The major contribution of this study is that the construction industry can efficiently select and implement LC tools and techniques allowing them to significantly reduce waste and improve project performance. Additionally, the well-structured validation process developed in this study has been proven efficient and valid and therefore can be used widely for other research in the future.
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.
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.
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.
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.
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.
Systemic Analyses and Indicators for Assessing Risks to Drinking Water Resources from Hydraulic Fracturing Chemicals
(North Dakota State University, 2021) Hill, Christopher Bruce
Hydraulic fracturing (HF) is a disruptive technology that has unlocked a vast amount of hydrocarbon resources, but presents risks to drinking water resources. Applying systemic risk assessment approaches to environmental and public health risks created by onshore unconventional oil and gas development (OUOGD) has not been explored and there are research gaps in system dynamics related to HF chemical transparency, variety, and hazard levels. The first objective of this research is to advance the application of systemic causation models to assess environmental and public health risks associated with OUOGD. A critical review of systemic causation models and their application for assessing these is presented. Holistic conceptual OUOGD process and control structure models are elucidated to provide a catalyst for future research. The second objective is to improve techniques and metrics used to measure and monitor systemic HF chemical transparency and feedback loops. After a comprehensive review of existing transparency indicators, two new metrics are developed and applied. The percent of wells with publicly disclosed ingredients increased from ~0% to 95%, and the average percent of HF fluid mass withheld on chemical disclosure forms decreased ~46.8%. The third objective is to provide context and analyze changes in HF chemical variety and influencing factors. A methodology for processing public HF chemical disclosure data into an updated unique HF chemical list is provided. The annual unique HF chemical counts were found to have dropped 32.3%. Identified HF chemicals are compared with reference chemical lists, including known food, cosmetics, and water-related additives and contaminants, for system and risk context. Approximately 70.0% of the HF chemicals are found in the reference chemical list. The last objective is to develop and apply a repeatable methodology for reporting relative HF chemical hazard levels to drinking water resources and characterize system dynamics. New individual parameter and aggregated risk indicators with associated approaches are provided. The aggregated metric indicated a 42.6% risk reduction. Overall, this research reveals past progress and methods for fostering future improvements related to HF chemical stewardship that can potentially be applied toward safer chemicals and transparency across all industries.
Weak Organic Acid Modified Granular Activated Carbon and Ceria Nanomaterial Embedded Graphene Oxide for Drinking Water Fluoride Removal
(North Dakota State University, 2021) Rashid, Umma Salma
Excess fluoride (F−) in drinking water leads to detrimental health effects including dental and skeletal fluorosis. More than 260 million people worldwide are affected by excess fluoride (>1.5 mg/L) in their drinking water. In this three-phase study, we have used modified activated carbon and cerium oxide nanomaterials for aqueous fluoride removal. The overarching goal of this research was to develop cost-effective drinking water fluoride removal technologies. In Phase I, a citric acid (0.3 M) modified granular activated carbon (CAGAC) was effectively used to remove >70% of aqueous fluoride within 60 min. The maximum adsorption capacity of CAGAC was two times (1.65 mg/g) that of unmodified GAC (0.88 mg/g). To address the need for defluoridation technologies adaptable in rural and socio-economically challenged communities, commonly available lime (Citrus aurantiifolia) juice was used in lieu of citric acid in Phase II of this research. The lime modified GAC (LGAC) showed an adsorption capacity of 1.63 mg/g. Both CAGAC and LGAC worked effectively over a wide range of pH (4-8) even though the point-of-zero-charge (PZC) was 4.89 for CAGAC and 3.05 for LGAC indicating that the fluoride removal was not controlled by electrostatic interaction alone, both surface adsorption and intra-particle diffusion also took part. In Phase III, graphene oxide-ceria (GO-CeO2) nanohybrid was used for fluoride removal as the activated carbon-based systems were slow in kinetics. The nanohybrid exhibited ultra-rapid kinetics for fluoride removal. The equilibrium (85% removal of 10 mg F−/L) was achieved within 1 minute which is the fastest kinetics for fluoride removal reported so far. The maximum fluoride adsorption capacity of GO-CeO2 nanohybrid was 8.61 mg/g at pH 6.5 and that increased to 16.05 mg/g at pH 4. The experimental results and characterization data indicated that both electrostatic interaction and surface complexation participated in the fluoride removal process. The oxygen ions present in CeO2 lattice were replaced by fluoride ions to make a stable CeF3 complex. During fluoride removal, the GO sheets acted as electron mediators and helped to reduce Ce4+ to Ce3+ at the CeO2 NPs-GO interface, and the additional Ce3+ enhanced fluoride removal by the nanohybrid.
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.
Post-Fire Damage and Corrosion in Structural Steels: Characterization and Prevention
(North Dakota State University, 2021) Sajid, Hizb Ullah
Steel structures frequently experience extreme service conditions such as fire accidents, corrosion, etc., resulting in significant deterioration, reduced service life, and increased maintenance cost. Almost 0.5 million structural fire accidents are reported annually in the U.S., inflicting a considerable toll on infrastructure. Similarly, corrosion-induced deterioration is the leading cause of premature failure in infrastructure and costs $22.6 billion in infrastructure maintenance annually. The bridge infrastructure vulnerability to these extreme service conditions is compounded by the aging bridge inventory (42% of the 617,000 bridges in the U.S. are 50 years or older). To improve the resilience and the “C-” infrastructure grade, it is vital to understand the material-scale damages and mechanisms induced by such extreme service conditions and develop efficient mitigation strategies. This dissertation adopts a two-prong approach to understand and predict the material-scale damage after exposure to fire accidents in steel structures and mitigate corrosion damage in steel/RC structures. Specifically, it aims to improve our current understanding of the post-fire mechanical behavior of steels and propose a microstructure-based approach for forensic analysis of fire-affected steel structures in phase-I. It further aims to mitigate corrosion in steel/RC structures by employing agriculturally-derived non-toxic materials and surface treatments in phase-II. Post-fire mechanical and microstructural investigations conducted in phase-I revealed that stress concentrations and fire-extinguishing methods significantly affect the post-fire mechanical behavior of structural steels, and post-fire steel microstructure can be utilized to accurately estimate the mechanical strength of structural steels without the knowledge of fire temperatures. The outcomes of phase-I of this dissertation can lead to accurate forensic fire investigations and usability determination of the fire-affected steel structures. The results obtained from phase-II of this dissertation revealed the role of surface treatments in improving the corrosion resistance and validated the performance of the agriculturally-derived materials such as corn-derived inhibitors and soy-protein coatings in lowering the corrosion damage in structural steels and embedded rebars by up to 90% without compromising the integrity of cement-based materials. These outcomes will lead to mitigating corrosion-induced deterioration in aging and new infrastructure. Overall, the outcomes of this dissertation contribute to improving infrastructure resilience and reducing maintenance costs.
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.
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.
An Investigation of the Mechanical Properties of Swelling Clays and Clay-Kerogen Interactions in Oil Shale: A Molecular Modeling and Experimental Study
(North Dakota State University, 2020) Thapa, Keshab Bahadur
This work provides an insight into how the molecular interactions influence macroscale properties of two materials: swelling clay and oil shale. Swelling clays cause enormous damage to infrastructure: buildings, roads, and bridges. Understanding the mechanisms are essential to prevent the detrimental effects and use of these clays for engineering applications. Our group studied the effect of fluid polarity on sodium montmorillonite (Na-MMT) swelling clay mineral using molecular modeling and experiments for bridging the molecular level behavior with the microstructure, swelling pressure, permeability, and compressibility. Various polar fluids (Dielectric Constant 110 to 20) found in landfill leachates are used. Our molecular dynamics (MD) simulations show that the nonbonded interactions of Na-MMT with polar fluids are higher than with low and medium polar fluids. These results are consistent with the results from Fourier transform infrared (FTIR) spectroscopy experiments. The polarity of the fluids and the fluid content influence the interlayer spacing, interlayer modulus, nonbonded interactions, and conformation as well as the shear strength parameters, the angle of internal friction (φ) and cohesion (c). Furthermore, the unconfined compressive strength experiments are used to evaluate the undrained cohesion at various swelling level. The nanomechanical properties, the modulus of elasticity (E) and hardness (H), of the undisturbed dry and saturated Na-MMT at various level of swelling are evaluated using nanoindentation experiments for the first time. The undrained cohesion, modulus of elasticity, and hardness decrease with increase in swelling level. Swelling controls the microstructure of Na-MMT clay, and the clay particles breakdown into smaller sizes with increase in swelling level. The Green River Formation located in the United States is the richest oil shale deposit in the world. Oil shale contains clay minerals, bitumen, and kerogen—a precursor to crude oil. A three-dimensional (3D) kerogen model is built from seven fragments, and the interactions of kerogen with Na-MMT is investigated using MD simulations to understand how the kerogen is bound to the clay mineral. The nonbonded interactions between Na-MMT and kerogen as well as among kerogen fragments are found. This work seeks to develop new methods to extract kerogen economically and efficiently.
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.
ADYTrack: A Model for Structural Analysis of Railroad Trackbed Using Random Finite Element Method
(North Dakota State University, 2019) Arshid, Asif
Railroads are playing pivotal role to the economic growth of United States and trackbeds ensure their safe and smooth operations. However, reliable trackbed performance prediction has always been challenging due to many reasons, for instance materials characterization, deteriorations of materials and geometries due to railways operation and environmental changes etc. All these factors exhibit varying levels of intrinsic variabilities and uncertainties. These variations and uncertainties are completely ignored in most of the state-of-the-practice problems due to lack of availability of robust models that can characterize variations in materials, geometries, and/or loadings. In this study, a Random Finite Element based three-dimensional numerical model, named ADYTrack, is developed for structural analysis of railroad trackbeds. Uniqueness of this model is the inclusion of materials’ intrinsic variabilities, geometric imperfections and/or uncertainties in axle loadings. The ADYTrack results, when compared with the analytical solution of a cantilever beam model, produced a maximum percentage difference of 0.7%; and 6% difference when compared with ANSYS software results for a single layer trackbed model; and a range of 5-20% difference was observed when validated against the actual field measurements. Sensitivity studies using RFEM based ADYTrack revealed that with the increasing variations in input parameters, measured by coefficient of variations (COV), the variations in output parameter also increased, and generally followed a bilinear trend with first linear component relatively insensitive up to around 30% COV of input parameters. However, beyond this limit, a considerable increase was observed in COVs of output parameters. For a COV of 80% in subgrade resilient modulus, a COV of 65% in vertical stress at the top of subgrade layer was observed. Additionally, the performance of any substructure layer found to be more sensitive to the variations in its own resilient modulus values. Furthermore, resilient modulus of subgrade layer was found to be the most influential input parameter, as revealed by many other studies, and so was its variations. To conclude, ADYTrack model can serve as a robust supplemental tool for railroad trackbed analysis, especially at locations that exhibit higher degrees of uncertainties and thus pose higher risk of public or infrastructure safety.
Traffic Monitoring System Using In-Pavement Fiber Bragg Grating Sensors
(North Dakota State University, 2019) Al-Tarawneh, Mu'ath
Recently, adding more lanes becomes less and less feasible, which is no longer an applicable solution for the traffic congestion problem due to the increment of vehicles. Using the existing infrastructure more efficiently with better traffic control and management is the realistic solution. An effective traffic management requires the use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles and their movement on the road. A three-dimension glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3D GFRP-FBG) is introduced for the traffic monitoring system. The proposed sensor network was installed for validation at the Cold Weather Road Research Facility in Minnesota (MnROAD) facility of Minnesota Department of Transportation (MnDOT) in MN. A vehicle classification system based on the proposed sensor network has been validated. The vehicle classification system uses support vector machine (SVM), Neural Network (NN), and K-Nearest Neighbour (KNN) learning algorithms to classify vehicles into categories ranging from small vehicles to combination trucks. The field-testing results from real traffic show that the developed system can accurately estimate the vehicle classifications with 98.5 % of accuracy. Also, the proposed sensor network has been validated for low-speed and high-speed WIM measurements in flexible pavement. Field testing validated that the longitudinal component of the sensor has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. A performed parametric study on the stability of the WIM system shows that the loading position is the most significant parameter affecting the WIM measurements accuracy compared to the vehicle speed and pavement temperature. Also the system shows the capability to estimate the location of the loading position to enhance the system accuracy.

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