Civil & Environmental Engineering Doctoral Workhttps://hdl.handle.net/10365/325482024-03-28T16:48:44Z2024-03-28T16:48:44ZBonding Performances of Epoxy-Based Composites Reinforced by Carbon NanotubesZhang, Daweihttps://hdl.handle.net/10365/337462024-03-21T22:55:28Z2022-01-01T00:00:00ZBonding Performances of Epoxy-Based Composites Reinforced by Carbon Nanotubes
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.
2022-01-01T00:00:00ZMultiscale Modeling of Conjugated Polymers Towards Predicting Their Multifunctional BehaviorsAlesadi, Amirhadihttps://hdl.handle.net/10365/337452024-03-21T22:44:55Z2022-01-01T00:00:00ZMultiscale Modeling of Conjugated Polymers Towards Predicting Their Multifunctional Behaviors
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.
2022-01-01T00:00:00ZIdentification, Categorization, and Prediction of Drought in Cold Climate RegionsBazrkar, Mohammad Hadihttps://hdl.handle.net/10365/335852024-01-12T15:29:54Z2021-01-01T00:00:00ZIdentification, Categorization, and Prediction of Drought in Cold Climate Regions
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.
2021-01-01T00:00:00ZOptimizing Selection and Implementation Protocols of Lean Construction Tools and Techniques for Rapid Initial SuccessesAslam, Mugheeshttps://hdl.handle.net/10365/335832024-01-12T15:22:44Z2021-01-01T00:00:00ZOptimizing Selection and Implementation Protocols of Lean Construction Tools and Techniques for Rapid Initial Successes
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.
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