Civil and Environmental Engineering

Recently, temporal data, especially time series data, have generated a lot of research and applications in the fields of statistics, physics, bioinformatics, oceanography, fluid mechanics, meteorology, and engineering. Compared to traditional datatypes such as audio, graphs, sensors, numerical data, images, time chronological data have complex properties such as periodicity, stochasticity, feedback loops, fractality, nonlinearity, chaos, bifurcation, and so on . Due to the inherent complexity of this type of data, quantifying its complexity has been a challenging research topic. In recent years, entropy characterization derived from information theory has been widely used to measure the complexity of such data. However, existing entropy methods mainly measure the complexity of individual sequential data. Therefore, this research proposes the Clustering-Weighted Multivariate Multiscale Sample Entropy (CWMMSE) method to measure the overall complexity of large-scale time-sequential dataset obtained by repeated observations or experiments. A measure that classifies time series into clusters under certain similarity measure and evaluates complexity by weighting each cluster based on size and frequency. It reduces the contribution of clusters that are too frequent or too infrequent to the complexity of the system. The effectiveness of the CWMMSE method is demonstrated using data constructed and collected from various types of nonlinear dynamics and transportation systems.

In the medieval times, a castle under attack would have its towers and walls bombarded by massive stones, launched by ever-developing siege engines which culminated in the highly powerful counterweight trebuchet, a machine capable of destroying walls and forcing defending garrisons to surrender. As artillery advanced, fortification design did too, and we may observe a vast portfolio of castle designs with features that enhanced their use as defensive fortifications, lordly residences, and symbols of conquest. Rectangular towers were replaced by circular and apsidal forms, great keeps were replaced by mural towers along curtain walls, and single lines of defense were replaced by concentric fortifications. In order to provide a structural perspective to the evolution of castle design and better understand the balance between offense and defense in medieval sieges, the aim of this work is to quantify the defensibility of medieval castles and the destructive power of the counterweight trebuchet. A novel methodology is applied to simplify and analyze the scenario of a trebuchet ammunition’s impact with a tower. This enables the derivation of simplified equations describing the impact robustness of towers and the impact force of a trebuchet, with a comparative analysis of tower geometries and trebuchet designs supported by historical evidence. The results of this work are applied to a selection of Welsh castles. Given a variety of castle forms and a chronology weighted by conflict and conquest, Wales provides valuable case studies for a holistic structural analysis of castle design evolution.

As the first nation to adopt Christianity in the early 4th century, Armenia has long held churchesas a core component of its cultural identity. Key architectural details and construction techniques define the distinctive Armenian church style and form throughout their historical evolution. The most readily recognizable of these unique forms is the conical-spherical church dome. Rising tall above the general massing, built of local tuff masonry, these domes help define the lasting legacy of Armenian churches. Built in a highly seismic region, these monuments have survived seventeen centuries worth ofearthquakes—lasting testaments to their engineering value. There exists a wide survey of unstudied Armenian conical-spherical domes, each with varying geometric parameters. However, traditional research in the field of historic structures focuses on singular case studies, largely owing to the static nature of analysis software. Thus, this novel geometry necessitates novel approaches. A parametric strategy is implemented to rapidly assess numerous sites, generate a robust understanding of the geometric properties, and examine their impact on structural resiliency. The aim of this research is to create a methodology for parametric structural study of historic masonry domes with complex geometry, namely the conical-spherical dome. Parametric strategies will enable exploration of multiple iterations of masonry dome geometric typologies in order to 1) rapidly inform understanding of the load bearing capacity; 2) investigate common threats to their structural condition; and 3) offer a framework for feasible reconstructions of these domes on existing ruins.

This study investigated the usage of Acidimicrobium sp. Strain A6 and the Feammox Process in the degradation of Poly- and Perfluoroalkyl Substances (PFAS) in sludge present in wastewater treatment plants (WWTPs). Per- and poly-fluoroalkyl substances (PFAS) are chemicals that have been recognized for their persistent nature and widespread within the environment. These environmental contaminators have posed a risk to society, finding themselves in food and causing health problems for animals and human beings. Their persistent nature means that they pose a serious risk to future generations who may face public health issues. This thesis provided a detailed overview of the preliminary findings, adopted methodology, and key implications. The main goal is to outline the integration between the Feammox process and Acidimicrobium sp. Strain A6 in PFAS bioremediation in biosolids. The main goal of the research is to introduce an innovative solution for the bioremediation of PFAS in biosolids. Findings from this thesis show that Acidimicrobium sp. Strain A6 can grow and be propagated in filter cake treatments amended with a ferric iron source and degrade PFAS. Results have also shown that treating the ferric iron source with a polyacrylic acid enhances the Feammox activity, including the growth of Acidimicrobium sp. Strain A6 and PFAS degradation, indicating directions for further improvements of this process.

Rising sea-level and potential alteration of hurricane climatology due to climate change will escalate the risk for coastal flooding and exploit currently delicate coastal infrastructures. Existing codes and risk mitigations in response to the anticipated worsening of coastal floods have been outlining design criteria for certain risk aversion levels and particular sea-level projections. However, designing for certain assumptions and static conditions without quantifying the optimal level of protection will either expose vulnerable coastal communities to unanticipated extreme damages or spend astronomical budgets and overprotect the region. Previous studies have proposed the adaptation of dynamic floodwalls (i.e., floodwalls can be incremented periodically as anticipated risk of flooding increases) with a cost-benefit analysis to prepare for the future climate and extreme flood events while designing for the optimal protection level; and progress has been made on determining dynamic floodwall policies under deep uncertainty with climate condition observations and agreement on future adaptations. In this paper, we propose a novel Deep Learning (DL) scheme that dramatically reduces the order of complexity of algorithms from previous studies and achieves suboptimal results. We apply a wide range of design strategies including DL for floodwall protection level in the New York City lower Manhattan tributaries focus area and incorporate inundation modeling for floodwall overtopping damage estimation to compare with the recent New York and New Jersey Harbor and Tributaries Focus Area Feasibility Study (NYNJHATS) developed by the US Army Corps of Engineers (USACE), New York District. We show that DL is a suboptimal strategy, with significantly less computational cost, to Reinforcement Learning (RL) with its benefit of adaptation of a 71% to 163% (95% CI) improvement compared to the current strategy proposed in NYNJHATS under moderate emissions.

Many bridges are built with steel plate girders that consist of thin webs, flanges, and stiffeners engineered to withstand significant shear loads. It is well-known that thin web plates are prone to buckling under shear loads, leading to reduced strength. However, recent research has challenged the shear mechanics used in existing design codes and standards to determine nominal shear capacity. The first objective of this thesis is thus to redefine “shear capacity” based on recent mechanics/behavior research and an extensive numerical study that uses 930 examples of plate girder panels fabricated/built in the last 10 years. Experimentally validated finite element models, and recent research on web shear buckling mechanics are employed to define and capture the shear strength thus creating 930 data points. The significance of this first objective is that it permits using extensive, proper and consistent data for developing models that predict shear capacity, which is the second objective of the thesis. This data is then used to develop alternative equations for predicting the shear capacity of steel plate girders, where the alternative equations are shown to have a better fit to the data than the existing codes and standards.

Urban centers contribute substantially to global greenhouse gas emissions, and with ongoing urbanization, the demand for infrastructure construction is set to rise. The construction and building sector significantly impact the environment, responsible for 36% of the world's energy consumption and 39% of its CO2 emissions. Annually, the global production of concrete and cement reaches 17.7 Gt and 4.1 Gt, respectively, making cement the second most utilized substance after water. Cement production alone contributes to 9-10% of worldwide CO2 emissions related to energy.This thesis comprises of two research projects to mitigate the environmental footprint of urban development. First, we develop the computer vision based deep learning model to estimate building material stocks (MS) in cities. Traditional methods of estimating MS often falter due to the lack of granular building data. We propose a novel solution by employing deep learning to derive MS estimates from readily available aerial and street-view imagery. Our methodology involves the development of two deep learning models that adeptly classify building types and predict floor areas, respectively. The model demonstrates reliable performance in predicting MS. Second, this thesis explores the environmental and cost benefits of alternative building materials: mass-timber and low-carbon cement. We create future scenarios and compare mass-timber and low-carbon cement in terms of carbon mitigation and cost savings. Results indicate that mass-timber buildings offer net-negative emissions and cost savings compared to conventional concrete-frame buildings.

Nature has developed abundant biological design motifs for arranging modest materials into complex, damage-tolerant architectures. The bouligand architecture found in the endocuticle of the mantis shrimp dactyl club is comprised of rows of fibrils forming sheets which stack helically atop each other. The double-bouligand architecture found in the scales of the coelacanth fish are similarly comprised of helically arranged sheets of collagen fibrils, however, each sheet is coupled with a second perpendicular layer of fibrils to form an orthogonal bilayer unit. The sinusoidal architecture is found at multiple length scales in several bivalve mollusks, for instance in the sinusoidal corrugations of the shell geometry, or the microstructure of the shell comprised of an undulating arrangement of aragonite platelets. Here, one-component and two-component robotic additive manufacturing processes were established to engineer architected concrete with bio-inspired toughening mechanisms including crack twisting, deflection, and shielding, occurring ahead and behind the crack tip. Experimental flexural and fracture tests (Mode-I) were conducted to characterize modulus of rupture, fracture toughness, and work of fracture. The architected concrete successfully increased fracture toughness and work of fracture compared to cast conventionally 3D-printed counterparts, without compromising flexural strength. The experimental findings were further studied through micro-computed tomography to examine the weak interface, and finite element simulations to interpret stress distributions. Harnessing the bio-inspired motifs can engender a pathway for leveraging reliable crack-resistance in the design of resilient additively manufactured concrete structures. Moreover, by leveraging bioinspired architectures, additively manufactured concrete infrastructure can be designed to have greater mechanical performance than what is achievable with conventional construction methods.