THE SEISMIC BEHAVIOR OF BURIED SEABED WALLS IN LIQUEFACTION SOIL

The present study aimed to investigate the seismic behavior of enclosed seawater walls, the buried site of which lies in liquefaction soil. An experimental specimen was manufactured and tested on the seismic table, and a numerical study was also modeled in the ABAQUS software based on the experimental outcomes. In both the experimental and numerical studies, a susceptible liquefaction layer around the root of the wall was considered due to the root lean soil leakage and large lateral pressure, and the soil behind the root caused the failure of the buried section. According to the results, the lateral movement significantly decreased due to the backing effect of this layer on the buried section of the wall. Furthermore, an active wedge was formed from the buried side to the back of the containment, and the braces were overwhelming due to the presence of the locks in the wedge and their movement along with the wedge. The displacement of the crown and foot of the wall decreased with the increased base acceleration and higher frequency of the entrance movement

Endogenous and exogenous factors driving bacterial community composition in aquatic ecosystems

The bacterial community (BC) composition in various habitats, ranging from ecosystems to host anatomy, plays an important role in determining the nature and role of BC function in the ecosystem or host. However, the relative importance of host endogenous and environmental exogenous factors in determining the composition of the BC in aquatic habitats (e.g., freshwater lakes, fish hosts) remains poorly understood. To address this knowledge gap, this thesis makes several contributions to the estimation of the relative effects of endo-exogenous factors in driving the BC composition in aquatic ecosystem. To test the impact of biotic and abiotic factors on aquatic bacterial biodiversity, I collected water samples from sixty southern Ontario lakes and their BC and microbial eukaryotic community (MEC) compositions were determined using high throughput metabarcode sequencing of 16S and 18S rRNA gene fragments. Additionally, I sampled skin and gut BCs belonging to 17 fish species from 11 families (7 orders) at three distinct Laurentian Great Lakes (LGLs) habitats (Detroit River, Lake Erie, Lake Ontario) along with the associated aquatic BCs at those sites. These data allowed me to assess the extent to which host habitat and phylogeny predict gut and skin BC similarity. Finally, to address the effect of host microbiome on gene expression pattern, I manipulated the gut BC in Chinook salmon (Oncorhynchus tshawytscha) families using antibiotic and probiotic treatments (with healthy controls) and assessed host gene expression using transcriptome sequencing (RNA-Seq) on hindgut tissue samples to identify differentially expressed (DE) host genes. Using a combination of parametric and non-parametric modelling, I showed deterministic processes (exogenous) prevail in shaping BC assembly in freshwater lakes, but that a combination of habitat-specific (e.g., microbial diversity associated with water) and species-specific (e.g., host ancestry, genotype, or diet) factors shape and promote divergence or convergence of the microbiome BC across host fish species. Additionally, I showed that daily administration of antibiotics and probiotics resulted in significant and predictable changes in fish gut and the surrounding aquatic microbiota. Normal microbiota depletion by antibiotics generally led to downregulation of immune response gene and upregulation of apoptotic processes, while probiotic treatment affected post-translation modification and inflammatory response genes (over-expressed). While these effects were mostly due to microbiome-mediated mechanisms, host-related mechanisms were also detected (i.e., family effects).In general, my thesis showed that BC composition in fish and lakes is regulated by assembly rules driven by exogenous abiotic and biotic factors (e.g., habitat, geography, microbial biodiversity, diet) and endogenous species-specific related factors (e.g., genetics, physiology, immunity). My work thus supports the deterministic view of BC composition variation across diverse habitats

A comparative experimental investigation of high-temperature effect on fibre concrete and high strength concrete using UT and CM methods

In this paper, a 28-day compressive strength test has been performed on samples including normal fibre concrete and high-strength concrete. The ultrasonic test (UT) as a non-destructive and compression machine (CM) as a destructive test were applied, and the results were compared. To investigate the effect of temperature, the samples were subjected to 200, 400, 600, 800, 1000, and 1200 degrees Celsius and the exposure time was equal to 30, 45, 60, 90, 120, and 180 minutes. Based on the results, it was observed that the minimum error observed between the UT and CM tests was 2.9 % and the maximum error between the two methods was 10.9 %, which shows the high accuracy of the ultrasonic testing method in determining the specimen’s strength. The average probable error of the method is determined to be around 6.8 %.Based on the results of the average decrease in compressive strength versus the heat exposure time, it is observed that the trend of changes and decrease in resistance over time for both types of tests is almost the same and has a negligible difference. At the end of 180 minutes of exposure, the resistance ratio for the ultrasonic test is 69.8 %, and 71.1 % for the compression machine. Furthermore, according to the average reduction in compressive strength due to heat exposure time, it has been observed that the results of the UT and UM tests have slight numerical differences, however, the trend of changes and reduction in resistance over time for both types of tests is almost the same. Finally, the accuracy of the UT in determining the compressive strength of specimens at high temperatures is fully confirmed

Assisting Sustainability Analysis of Forest Bioenergy Supply Chains using Mathematical Optimization

Changes in the global climate and forest management practices have given rise to increasing numbers and severity of wildfires. More than five million acres burned in the United States in 2017, while in Canada 7.4 million acres burned. In particular, an increasing amount of dead woody biomass is a key factor in forest fire hazards. The call for mitigating the effects of climate change, specifically focusing on reducing the risk of wildfires, has attracted considerable global attention toward renewable energy sources. The objective of this research is to provide decision makers in private industry and governmental agencies the ability to reliably assess economic, environmental, and social criteria simultaneously while optimizing bio-oil supply chains in managing the land and forests to decrease wildfire risks. An optimized biomass to bio oil supply chain is presented by using a mathematical problem considering economic, environmental, and social criteria. The focus of the application of this work is on northwest Oregon forests. The production of bio-oil is not only able to help mitigate climate change impacts such as forest fire hazards, but it can also improve energy independence, employment opportunities, and economic development. To extend prior related research, a single objective mathematical model is first presented, which relaxes a limitation of prior mathematical models for bio-oil supply chain problems by considering carbon cost as a part of the total supply chain cost. Since the model is a mixed integer linear programming problem, a metaheuristic optimization approach (genetic algorithm) is designed to obtain an optimized solution. The proposed mathematical model can be applied in the design of a biomass to bio-oil supply chain including mobile refineries, in which total cost consists of logistics cost and carbon cost. Decision makers will be able to apply the proposed genetic algorithm for large scale problems to overcome restrictions of exact methods. As the demand for sustainable supply chains continues, logistics problems must be designed to balance solutions across the three pillars of sustainability: the economy, environment, and society. Thus, a multi-objective mathematical model is next developed for a bio oil supply chain, which includes six levels: harvesting sites, collection sites, mobile refineries, fixed refineries, distribution centers, and residential areas. The branch-and-cut search in CPLEX software solves the proposed model using data from northwest Oregon forests. The model obtains optimal values for three decision variables, i.e., mass of biomass to be transported, mass of bio-oil to be transported, and the facility locations, to simultaneously optimize total cost, carbon footprint, and number of jobs created. From evaluation of the model, it is found that supplementing a traditional bio-oil supply chain with mobile refineries has the potential to significantly reduce the cost of bio-oil. Sensitivity analysis is performed to evaluate the effect of key parameters on supply chain objectives under different scenarios. It was also found that the percentage yield parameter and mobile refinery capacity have a more significant effect on the selected objectives than the other parameters tested. Based on the supply chain modeling, the behavior of the predicted cost of bio-oil, carbon footprint, and number of jobs created is intuitive with respect to the changes in the model parameters. Further, the sensitivity analysis results show that the cost of bio oil predicted by the mathematical model falls in the cost interval found in the market and research literature. In addition to reducing wildfire risks and energy dependence by collecting combustible forest biomass, the research result shows that consideration of societal aspects in bio-oil supply chains can provide a competitive cost of bio-oil. Exploration of mobile refineries is a focus here to elucidate bio-oil supply chain sustainability performance through mathematical modeling, and has not been previously reported in literature. The lack of access to the conversion processes prevented a more accurate estimation of the cost of bio-oil. To improve this limitation, modeling the parameters of bio-oil supply chains using stochastic approaches in future research would allow for a more in-depth investigation of tradeoffs between objectives

A parameter-tuned genetic algorithm for vendor managed inventory model for a case single-vendor single-retailer with multi-product and multi-constraint

This paper develops a single-vendor single-retailer supply chain for multi-product. The proposed model is based on Vendor Managed Inventory (VMI) approach and vendor uses the retailer's data for better decision making. Number of orders and available capital are the constraints of the model. In this system, shortages are backordered; therefore, the vendor’s warehouse capacity is another limitation of the problem. After the model formulation, an Integer Nonlinear Programming problem will be provided; hence, a genetic algorithm has been used to solve the model. Consequently, order quantities, number of shipments received by a retailer and maximum backorder levels for products have been determined with regard to cost consideration. Finally, a numerical example is presented to describe the sufficiency of the proposed strategy with respect to parameter-tuned by response surface methodology (RSM).</p