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No / It is known that lime was added to historic Roman cement render mortars. The focus of this work is the influence of the combination of NHL5 and CL90 with Roman cement in mortars for restoration; however, the results indicate a wider potential for render applications in general. It is shown that simply adding lime to Roman cement does not retard its hydration and yields mortars where the binding action of the cement is compromised by the mixing process. If the cement is retarded by means of a pre-hydration process, hybrid mortars can be produced with improved workability and workable life as well as permitting the fine control of strength and moisture transport.

The objective of this laboratory research was to investigate the effects of a higher water-cementitious materials ratio on selected properties of concrete mixtures comprising natural pozzolans. The scope of work included testing of six concrete mixtures, including one for each of three natural pozzolans at two water-cementitious materials ratios of 0.44 and 0.48 and one concrete mixture without pozzolan at a water-cementitious materials ratio of 0.44, which was treated as a baseline in this research. The stiffness and strength of each concrete mixture were measured at 1, 3, and 7 days using concrete specimens that were cast immediately after mixing. Additionally, to investigate the effects of delayed casting time, slump was measured at 0, 15, 30, 45, and 60 minutes after mixing, and cylinders were cast at 15, 30, 45, and 60 minutes for stiffness and strength testing at 7 days. Two mixtures comprising natural pozzolan experienced greater slump loss, on average, than the baseline mixture, while all of the other mixtures experienced less slump loss, on average, than the baseline mixture. Overall, the slump losses of mixtures comprising natural pozzolans were 121% and 71% of that of the baseline mixture for water-cementitious materials ratios of 0.44 and 0.48, respectively. Modulus of elasticity values ranged from 1692 ksi to 1794 ksi for mixtures comprising natural pozzolan compared to a value of 1791 ksi for the baseline mixture at 7 days. Compressive strength values ranged from 4087 psi to 4152 psi for mixtures comprising natural pozzolan compared to a value of 4795 psi for the baseline mixture at 7 days. The modulus of elasticity values of mixtures comprising pozzolans were 97% and 94% of that of the baseline mixture for water-cementitious materials ratios of 0.44 and 0.48, respectively, at 7 days. Similarly, the compressive strength values of mixtures comprising pozzolans were 86% and 71% of that of the baseline mixture for water-cementitious materials ratios of 0.44 and 0.48, respectively, at 7 days. Comparisons of the 7-day stiffness and strength results associated with casting delay time for mixtures comprising natural pozzolan with those of the baseline mixture indicate that all mixtures comprising natural pozzolan exhibited lower modulus of elasticity and compressive strength than the baseline mixture. Overall, the modulus of elasticity values of mixtures comprising natural pozzolans were 94% and 84% of that of the baseline mixture for water-cementitious materials ratios of 0.44 and 0.48, respectively, for a casting delay time of an hour. Similarly, the compressive strength values of mixtures comprising natural pozzolans were 85% and 64% of that of the baseline mixture for water-cementitious materials ratios of 0.44 and 0.48, respectively, for a casting delay time of an hour.

Recent advancements in sensing technologies offer new opportunities for quality improvement and assurance in manufacturing and service systems. The sensor advances provide a vast amount of data, accommodating quality improvement decisions such as fault diagnosis (root cause analysis), and real-time process monitoring. These quality improvement decisions are typically made based on the predictive analysis of the sensor data, so called sensor-based predictive analytics. Sensor-based predictive analytics encompasses a variety of statistical, machine learning, and data mining techniques to identify patterns between the sensor data and historical facts. Given these patterns, predictions are made about the quality state of the process, and corrective actions are taken accordingly. Although the recent advances in sensing technologies have facilitated the quality improvement decisions, they typically result in high dimensional sensor data, making the use of sensor-based predictive analytics challenging due to their inherently intensive computation. This research begins in Chapter 1 by raising an interesting question, whether all these sensor data are required for making effective quality improvement decisions, and if not, is there any way to systematically reduce the number of sensors without affecting the performance of the predictive analytics? Chapter 2 attempts to address this question by reviewing the related research in the area of signal processing, namely, compressive sensing (CS), which is a novel sampling paradigm as opposed to the traditional sampling strategy following the Shannon Nyquist rate. By CS theory, a signal can be reconstructed from a reduced number of samples, hence, this motivates developing CS based approaches to facilitate predictive analytics using a reduced number of sensors. The proposed research methodology in this dissertation encompasses CS approaches developed to deliver the following two major contributions, (1) CS sensing to reduce the number of sensors while capturing the most relevant information, and (2) CS predictive analytics to conduct predictive analysis on the reduced number of sensor data. The proposed methodology has a generic framework which can be utilized for numerous real-world applications. However, for the sake of brevity, the validity of the proposed methodology has been verified with real sensor data associated with multi-station assembly processes (Chapters 3 and 4), additive manufacturing (Chapter 5), and wearable sensing systems (Chapter 6). Chapter 7 summarizes the contribution of the research and expresses the potential future research directions with applications to big data analytics. / Ph. D.

Exploring the mechanical properties of soft tissues under compressive loading is crucial for understanding their role in automobile incidents. Soft tissues, which serve as cushions or padding between bone and vehicle interiors, significantly influence contact duration and forces, thereby altering incident kinematics and injury risk assessment. In this investigation, muscle and soft connective tissues from post-mortem human subjects (PMHS) forearms were excised and subjected to compression and indentation testing methods at various rates and strains. Anthropomorphic Test Devices (ATDs) upper extremity foam and vinyl foam composite material surrogate tissues underwent similar testing for comparison. High impact rates simulating those in high-speed car collisions were achieved using a custom-built drop tower. The results revealed substantial differences in stiffness between soft tissues and ATD materials across most loading rates and strains, although some exceptions were noted at higher rates and strains. Indentation and modified Zener models were used to quantify material parameters. The indentation model could characterize human muscle, soft connective tissues and ATD vinyl foam composites, but fell short with ATD foam materials. The Zener model effectively derived material parameters for the tested human tissues but encountered difficulties characterizing both ATD materials. This highlights the need for further refinement to develop a constitutive model for both materials. These findings provide a solid basis for advancing ATD surrogate materials and have broader implications for soft tissue research. Moreover, this work represents a crucial step towards enhancing safety standards in the automotive industry. / Thesis / Master of Science in Biomedical Engineering / Soft tissues are crucial in mitigating impact effects in various loading scenarios, yet their specific roles are complex and poorly understood. Understanding soft tissues' role in these loading scenarios is critical for understanding injury risk tolerances. This study aimed to characterize muscle and soft connective tissue behaviour during compressive loading scenarios using various techniques and modelling approaches. This was done through compressive loading tests on soft tissues and comparing these same tests with data from current crash test dummy surrogate tissues. The results showed that the soft tissues were less stiff than the crash test dummy materials in most scenarios. It was also apparent that different stiffnesses were seen depending on soft connective tissue and muscle tissue composition. This study provides insights into the rate dependence of materials, alongside the relevance of how different compositions affect their loading properties. This characterization also revealed significant discrepancies between the responses of current surrogates and human muscle and soft connective tissues. This work offers valuable observations and data for refining ATD surrogates and enhancing their fidelity in simulating real-world impact scenarios. Such advancements are pivotal for improving safety standards.

Yes / Demolition and reconstruction of degrading structures alongside with the repetitive repair, maintenance, and renovation applications create significant amounts of construction and demolition waste (CDW), which needs proper tackling. The main emphasis of this study has therefore been placed on the development of concrete mixtures with components (i.e., aggregates and binder) coming entirely from CDW. As the binding phase, powdered CDW-based masonry units, concrete and glass were used collectively as precursors to obtain geopolymer binders, which were then incorporated with CDW-based fine and coarse concrete aggregates. Together with the entirely CDW-based concretes, designs were also proposed for companion mixtures with mainstream precursors (e.g., fly ash and slag) occupying some part of the CDW-based precursor combination. Sodium hydroxide (NaOH), sodium silicate (Na2SiO3) and calcium hydroxide (Ca[OH]2) were used at various concentrations and combinations as the alkaline activators. Several factors that have impact on the compressive strength results of concrete mixtures, such as mainstream precursor replacement rate, al-kaline molar concentrations, aggregate-to-binder ratios and curing conditions, were considered and these were also backed by the micro-structural analyses. Our results showed that through proper optimiza-tion of the design factors, it is possible to manufacture concrete mix-tures entirely out of CDW with compressive strength results able to reach up to 40 MPa under ambient curing. Current research is believed to be very likely to promote more innovative and up-to-date techniques to upcycle CDW, which are mostly downcycled through basic practices of road base/sub-base filling, encouraging further research and increas-ing the awareness in CDW issue.

No / The main aim of this study is to investigate the effects of nano-SiO2 (NS), nano-Al2O3 (NA), nano-TiO2 (NT) and nano-Fe2O3 (NF) particles in single, binary, ternary, and quaternary combinations on compressive, splitting tensile, and flexural strengths of concrete. The residual compressive strength of control and nano-added concretes are also determined at 300, 500, and 800 °C elevated temperatures. Furthermore, X-ray diffraction (XRD) and scanning electron microscope (SEM) analyses have been conducted to examine the chemical composition and microstructure of concrete samples. The main parameters investigated were the amount and various combinations of NS, NA, NT and NF, producing thirty-one concrete batches, one control and thirty NS, NA, NT and NF added concrete mixes. The total nanoparticle amounts in the concrete mixes of 0.5%, 1%, and 1.5% by weight of cement were studied. A total of 558 concrete specimens with nanoparticles were tested at 28 days to determine compressive, splitting tensile, flexural, and residual compressive strength of concretes at ambient and elevated temperatures. It can be clearly concluded that NS and NA particles are more effective than NT and NF particles in improving the mechanical properties of concrete. The largest increase in compressive, splitting tensile, and flexural strength was obtained for 1.5% of NS and NA hybrid combination as 13.95%, 18.55%, and 21.88%, respectively. Furthermore, the residual compressive strength of single and hybrid nano-added concrete specimens significantly reduced, especially at 800 °C. Although the largest decrease in residual compressive strength of 57.65% was recorded for control concrete, the lowest reduction of 41.59% was observed for concrete with 1.5% of NS and NA hybrid combination at 800 °C.

Yes / Degrading infrastructure and applications of structural demolition create tremendous amounts of construction and demolition waste (CDW) all around the world. To address this issue in an effective way, recycling CDW in a most appropriate way has become a global concern in recent years. To this end, this study focused on the valorization of CDW-based materials such as tile, bricks, glass, and concrete in the development of geopolymer mortars. CDWs were first collected from demolition zone and then subjected to crushing-milling operations. To investigate the influence of slag (S) addition to the mixtures, 20% S substituted mixture designs were also made. Fine recycled concrete aggregates (FRCA) obtained from crushing and sieving of the waste concrete were used as the aggregate. A series of mixtures were designed using different proportions of three distinct alkali activators such as sodium hydroxide (NaOH), sodium silicate (Na2SiO3), and calcium hydroxide (CH; Ca(OH)2). To improve their applicability, the mixtures were left to cure at room temperature rather than the heat curing which is frequently applied in the literature. After 28 days ambient curing, the 100% CDW-based geopolymer mortar activated with three different activators reached a compressive strength of 31.6 MPa, whereas the 20% S substituted geopolymer mortar showed a 51.9 MPa compressive strength. While the geopolymer mortars activated with only NaOH exhibited poor performance, it was found that the use of Na2SiO3 and CH improved the mechanical performance. Main geopolymerization products were related to NASH (Sodium alumino-silicate hydrate), CASH (Calcium alumino-silicate hydrate), and C(N)ASH gel formations. Results demonstrated that mixed CDWs can be employed in the manufacturing geopolymers, making them potential alternatives to Portland cement (PC)-based systems by being eco-friendly, energy-efficient, and comparable in compressive strength. / This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 894100.

Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as a neural signal, electrocardiogram (ECG), and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increases, data volume is increased resulting in impermissible transmitting power. This thesis work presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. This thesis work also includes the (reverse electrowetting-on-dielectric) REWOD based energy harvesting model for self-powered bio-sensor which utilizes the electrical energy generated through the process of conversion of mechanical energy to electrical energy. This REWOD based energy harvesting model can be a good alternative to battery usage, particularly for the bio-wearable applications. The comparative analysis of the results generated for voltage, current and capacitance of the rough surface model is compared to that of results of planar surface REWOD.