Yes / Geopolymers are mostly produced with main-stream precursors such as fly ash and slag. These precursors are successfully used and competitively demanded by the cement industry. Development of geopolymers from alternative precursors is appealing. The main aim of this work is the development of geopolymers with construction and demolition waste-based precursors including masonry units (red clay brick, roof tile, hollow brick) and glass. Different curing temperatures (50, 65, 75, 85, 95, 105, 115, 125 oC), curing periods (24, 48, 72 h), and Na concentrations (10, 12, 15%) of alkaline activator (NaOH) were employed. Compressive strength testing and microstructural investigations were performed including X-ray diffraction, thermogravimetry and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Results showed that depending on the type of precursor (hollow brick), curing temperature/period (115 oC/24 h) and concentration of alkaline activator (12%), it is possible to obtain compressive strength results more than 45 MPa. Hollow brick is the most successful precursor resulting in higher compressive strength results thanks to a more compact microstructure. The strength performance of red clay brick and roof tile is similar. The compressive strength results of geopolymers with glass precursor are lower, most probably due to significantly coarser particles of glass used. The main reaction products of red clay brick-, roof tile- and hollow brick-based geopolymers are sodium aluminosilicate hydrate (N-A-S-H) gels with zeolite-like structures while they are sodium silicate gels in the case of glass-based geopolymers. Our findings showed that CDW-based materials can be used successfully in producing geopolymers. Current research is believed to help raise awareness in novel routes for the effective utilization of such wastes which are realistically troublesome and attract further research on the utilization of CDW-based materials in geopolymer production. / The authors gratefully acknowledge the financial assistance of the Scientific and Technical Research Council (TUBITAK) of Turkey and British Council provided under projects: 117M447 and 218M102.

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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.

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.

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.

A teoria de Compressive Sensing proporciona uma nova estratégia de aquisição e recuperação de dados com bons resultados na área de processamento de imagens. Esta teoria garante recuperar um sinal com alta probabilidade a partir de uma taxa reduzida de amostragem por debaixo do limite de Nyquist-Shanon. O problema de recuperar o sinal original a partir das amostras consiste em resolver um problema de otimização. O método de Gradiente Espectral Projetado é um método para minimizar funções suaves em conjuntos convexos que tem sido aplicado com frequência ao problema de recuperar o sinal original a partir do sinal amostrado. Este trabalho dedica-se ao estudo da aplicação do Método do Gradiente Espectral Projetado ao problema de Compressive Sensing. / The theory of compressive sensing has provided a new acquisition strategy and data recovery with good results in the image processing area. This theory guarantees to recover a signal with high probability from a reduced sampling rate below the Nyquist-Shannon limit. The problem of recovering the original signal from the samples consists in solving an optimization problem. The Spectral Projected Gradient (SPG) is a method to minimize continuous functions over convex sets which often has been applied to the problem of recovering the original signal from sampled signals. This work is dedicated to the study and application of the Spectral Projected Gradient method to Compressive Sensing problems.

A teoria de Compressive Sensing proporciona uma nova estratégia de aquisição e recuperação de dados com bons resultados na área de processamento de imagens. Esta teoria garante recuperar um sinal com alta probabilidade a partir de uma taxa reduzida de amostragem por debaixo do limite de Nyquist-Shanon. O problema de recuperar o sinal original a partir das amostras consiste em resolver um problema de otimização. O método de Gradiente Espectral Projetado é um método para minimizar funções suaves em conjuntos convexos que tem sido aplicado com frequência ao problema de recuperar o sinal original a partir do sinal amostrado. Este trabalho dedica-se ao estudo da aplicação do Método do Gradiente Espectral Projetado ao problema de Compressive Sensing. / The theory of compressive sensing has provided a new acquisition strategy and data recovery with good results in the image processing area. This theory guarantees to recover a signal with high probability from a reduced sampling rate below the Nyquist-Shannon limit. The problem of recovering the original signal from the samples consists in solving an optimization problem. The Spectral Projected Gradient (SPG) is a method to minimize continuous functions over convex sets which often has been applied to the problem of recovering the original signal from sampled signals. This work is dedicated to the study and application of the Spectral Projected Gradient method to Compressive Sensing problems.