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.
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.
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.
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.
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.
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.
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.
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.

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Browsing Civil & Environmental Engineering Doctoral Work by browse.metadata.department "Civil, Construction, and Environmental Engineering"