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Araya-Letelier, G., Maturana, P., Carrasco, M., Antico, F. C., & Gomez, M. S. (2019). Mechanical-Damage Behavior of Mortars Reinforced with Recycled Polypropylene Fibers. Sustainability, 11(8), 17 pp.
Abstract: Commercial polypropylene fibers are incorporated as reinforcement of cement-based materials to improve their mechanical and damage performances related to properties such as tensile and flexural strength, toughness, spalling and impact resistance, delay formation of cracks and reducing crack widths. Yet, the production of these polypropylene fibers generates economic costs and environmental impacts and, therefore, the use of alternative and more sustainable fibers has become more popular in the research materials community. This paper addresses the characterization of recycled polypropylene fibers (RPFs) obtained from discarded domestic plastic sweeps, whose morphological, physical and mechanical properties are provided in order to assess their implementation as fiber-reinforcement in cement-based mortars. An experimental program addressing the incorporation of RPFs on the mechanical-damage performance of mortars, including a sensitivity analysis on the volumes and lengths of fiber, is developed. Using analysis of variance, this paper shows that RPFs statistically enhance flexural toughness and impact strength for high dosages and long fiber lengths. On the contrary, the latter properties are not statistically modified by the incorporation of low dosages and short lengths of RPFs, but still in these cases the incorporation of RPFs in mortars have the positive environmental impact of waste encapsulation. In the case of average compressive and flexural strength of mortars, these properties are not statistically modified when adding RPFs.
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Billi, M., Mascareno, A., Henriquez, P. A., Rodriguez, I., Padilla, F., & Ruz, G. A. (2022). Learning from crises? The long and winding road of the salmon industry in Chiloe Island, Chile. Mar. Pol., 140, 105069.
Abstract: The rapid development of salmon aquaculture worldwide and the growing criticism of the activity in recent decades have raised doubts about the capacity of the sector to learn from its own crises. In this article, we assess the discursive, behavioral and outcome performance dimensions of the industry to identify actual learning and lessons to be learned. We focus on the case of Chiloe Island, Chile, a global center of salmon production since 1990 that has gone through two severe crises in the last 15 years (2007-2009 ISAV crisis and 2016 red tide crisis). On the basis of a multi-method approach combining qualitative analysis of interviews and statistical data analysis, we observe that the industry has discursively learned the relevance of both self-regulation and the wellbeing of communities. However, at the behavioral and outcome performance levels, the data show a highly heterogeneous conduct that questions the ability of the sector as a whole to learn from crises. We conclude that detrimental effects for ecosystems and society will increase if learning remains at the level of discourses. Without significant changes in operational practices and market performance there are no real perspectives for the sustainability of the industry. This intensifies when considering the uneven responses to governance mechanisms. The sector needs to adapt its factual performance to sustainable goals and reflexively monitor this process. The first step for achieving this is to produce reliable data to make evidence-based decisions that align the operational dynamics of the entire sector with a more sustainable trajectory in the near future, as well as advancing towards hybrid and more reflexive governance arrangements.
Keywords: Crisis; Learning; Discourse; Behavior; Outcome performance; Aquaculture; Salmon industry; Governance; Regulation
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Caceres, C., Morgado, M. D. G., Bozo, F. C., Piletsky, S., & Moczko, E. (2022). Rapid Selective Detection and Quantification of beta-Blockers Used in Doping Based on Molecularly Imprinted Nanoparticles (NanoMIPs). Polymers, 14(24), 5420.
Abstract: Human performance enhancing drugs (PEDs), frequently used in sport competitions, are strictly prohibited by the World Anti-Doping Agency (WADA). Biological samples collected from ath-letes and regular patients are continuously tested regarding the identification and/or quantification of the banned substances. Current work is focused on the application of a new analytical method, molecularly imprinted nanoparticles (nanoMIPs), to detect and determine concentrations of certain prohibited drugs, such as B-blockers, in water and human urine samples. These medications are used in the treatment of cardiovascular conditions, negative effects of adrenaline (helping to relief stress), and hypertension (slowing down the pulse and softening the arteries). They can also significantly increase muscle relaxation and improve heart efficiency. The new method of the detection and quantification of B-blockers is based on synthesis, characterization, and implementation of nanoMIPs (so-called plastic antibodies). It offers numerous advantages over the traditional methods, including high binding capacity, affinity, and selectivity for target molecules. Additionally, the whole process is less complicated, cheaper, and better controlled. The size and shape of the nanoMIPs is evaluated by dynamic light scattering (DLS) and transmission electron microscope (TEM). The affinity and selectivity of the nanoparticles are investigated by competitive pseudo enzyme-linked immunosorbent assay (pseudo-ELISA) similar to common immunoassays employing natural antibodies. To provide reliable results towards either doping detection or therapeutic monitoring using the minimal invasive method, the qualitative and quantitative analysis of these drugs is performed in water and human urine samples. It is demonstrated that the assay can detect B-blockers in water within the linear range 1 nmolmiddotL(-1)-1 mmolmiddotL(-1) for atenolol with the detection limit 50.6 ng mL(-1), and the linear range 1 mmolmiddotL(-1)-10 mmolmiddotL(-1) for labetalol with the detection limit of 90.5 ngmiddotmL(-1). In human urine samples, the linear range is recorded in the concentration range 0.1 mmolmiddotL(-1)-10 nmolmiddotL(-1) for atenolol and 1 mmolmiddotL(-1)-10 nmolmiddotL(-1) for labetalol with a detection limit of 61.0 ngmiddotmL(-1)for atenolol and 99.4 ngmiddotmL(-1) for labetalol.
Keywords: doping in sports; performance enhancing drugs (PEDs); beta-blockers; atenolol; labetalol; molecularly imprinting nanoparticles (nanoMIPs); enzyme-linked immunosorbent assay (ELISA); “pseudo” enzyme-linked immunosorbent assay (pseudo-ELISA); dynamic analysis light scattering (DLS); transmission electron microscope (TEM)
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Cataldo-Born, M., Araya-Letelier, G., & Pabon, C. (2016). Obstacles and motivations for earthbag social housing in Chile: energy, environment, economic and codes implications. Rev. Constr., 15(3), 17–26.
Abstract: Chile presents a social housing deficit that needs to be addressed with solutions that increase habitability and environmental benefits. This paper addresses the benefits of implementing earthbag buildings as an option to mitigate the existing social housing deficit in Chile. A literature review presents details on the use of earthbag buildings around the world, and motivations and obstacles for implementing earthbag buildings in Chile. In particular, a case study was simulated to compare an earthbag social house to a reinforced brick masonry social house in terms of environmental and economic performances such as CO2 emissions, energy and costs. It is concluded that both alternatives generate similar CO2 emissions, but the earthbag social house can save up to 20% of energy during its life cycle. In economic terms, the earthbag social house generates savings of 50% and 38% for initial investment and life cycle cost, respectively, compared to the reinforced brick masonry social house. The implementation of earthbag social housing projects would be encouraged by the development of a Chilean building code for earthbag design that provides guidance on the safe use of this technique in a seismic country.
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Fustos-Toribio, I., Manque-Roa, N., Vasquez Antipan, D., Hermosilla Sotomayor, M., & Gonzalez, V. L. (2022). Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes. Nat. Hazards Earth Syst. Sci., 22(6), 2169–2183.
Abstract: Rainfall-induced landslides (RILs) are an issue in the southern Andes nowadays. RILs cause loss of life and damage to critical infrastructure. Rainfall-induced landslide early warning systems (RILEWSs) can reduce and mitigate economic and social damages related to RIL events. The southern Andes do not have an operational-scale RILEWS yet. In this contribution, we present a pre-operational RILEWS based on the Weather and Research Forecast (WRF) model and geomorphological features coupled to logistic models in the southern Andes. The models have been forced using precipitation simulations. We correct the precipitation derived from WRF using 12 weather stations through a bias correction approach. The models were trained using 57 well-characterized RILs and validated by ROC analysis. We show that WRF has strong limitations in representing the spatial variability in the precipitation. Therefore, accurate precipitation needs a bias correction in the study zone. We used accurate precipitation simulation and slope, demonstrating a high predicting capacity (area under the curve, AUC, of 0.80). We conclude that our proposal could be suitable at an operational level under determined conditions. A reliable RIL database and operational weather networks that allow real-time correction of the mesoscale model in the implemented zone are needed. The RILEWSs could become a support to decision-makers during extreme-precipitation events related to climate change in the south of the Andes.
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Gallegos, M. F., Araya-Letelier, G., Lopez-Garcia, D., & Parra, P. F. (2023). Collapse Assessment of Mid-Rise RC Dual Wall-Frame Buildings Subjected to Subduction Earthquakes. Buildings, 13(4), 880.
Abstract: In Chile, office buildings are typically reinforced concrete (RC) structures whose lateral load-resisting system comprises core structural walls and perimeter moment frames (i.e., dual wall-frame system). In the last 20 years, nearly 800 new dual wall-frame buildings have been built in the country and roughly 70% of them have less than ten stories. Although the seismic performance of these structures was deemed satisfactory in previous earthquakes, their actual collapse potential is indeed unknown. In this study, the collapse performance of Chilean code-conforming mid-rise RC buildings is assessed considering different hazard levels (i.e., high and moderate seismic activity) and different soil types (i.e., stiff and moderately stiff). Following the FEMA P-58 methodology, 3D nonlinear models of four representative structural archetypes were subjected to sets of Chilean subduction ground motions. Incremental dynamic analysis was used to develop collapse fragilities. The results indicate that the archetypes comply with the 'life safety' risk level defined in ASCE 7, which is consistent with the observed seismic behavior in recent mega-earthquakes in Chile. However, the collapse risk is not uniform. Differences in collapse probabilities are significant, which might indicate that revisions to the current Chilean seismic design code might be necessary.
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Gallegos, M. F., Araya-Letelier, G., Lopez-Garcia, D., & Parra, P. F. (2023). Seismic collapse performance of high-rise RC dual system buildings in subduction zones. Case Stud. Constr. Mater., 18, e02042.
Abstract: The satisfactory 'collapse prevention' performance level of reinforced concrete (RC) buildings has been widely recognized during recent earthquakes in Chile. However, there is limited research on the actual level of seismic collapse protection. In this study, the seismic collapse behavior of high-rise RC dual wall-frame buildings representative of the Chilean inventory is quantitatively eval-uated. A suite of four 16-story structural archetypes was carefully selected and code-based designed assuming two different locations (i.e., high and moderate seismicity zones) and two different soil types (i.e., very stiff and moderately stiff soils). The archetypes were analyzed considering the latest developments in performance-based earthquake engineering implementing incremental dynamic analyses for 3D nonlinear models with sets of Chilean subduction ground motions. Results, expressed in terms of the probability of collapse conditioned on the Maximum Considered Earthquake (MCE) hazard level (<10%) and the collapse probability in 50 years (<1%), showed that all archetypes fully met the targets specified by ASCE 7 for an acceptable 'life safety' risk level. These results indeed explain why a very small number of RC building collapses was observed in the recent megathrust earthquakes (Mw>8.0) in Chile. Nevertheless, it was also found that the seismic collapse performance is not uniform, due mainly to the soil type. This observation suggests that the design spectra indicated by the Chilean seismic design code for buildings might need to be revised.
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Simon, F., Ordonez, J., Girard, A., & Parrado, C. (2019). Modelling energy use in residential buildings: How design decisions influence final energy performance in various Chilean climates. Indoor Built Environ., 28(4), 533–551.
Abstract: To reduce the energy consumption in buildings, there is a demand for tools that identify significant parameters of energy performance. The work presents the development and validation of a simulation model, called MEEDI, and graphical figures for the parametric sensitivity investigation of energy performance in different climates in Chile. The MEEDI is based on the ISO 13790 monthly calculation method of building energy use with two improved procedures for the calculation of the heat transfer through the floor and the solar heat gains. The graphical figures illustrate the effects of climate conditions, envelope components and window size and orientation on the energy consumption. The MEEDI program can contribute to find the best solution to increase energy efficiency in residential buildings. It can be adapted for various parameters, making it useful for future projects. The economic viability of specific measures for building envelope materials was analysed. Payback periods range from 5 to 27 years depending on the location and energy scenario. The study illustrates how building design decisions can have a significant impact on final energy performance. With simple envelope components modification, valuable energy gains and carbon emission reductions can be achieved in a cost-effective manner in Chile.
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Simon, F., Ordonez, J., Reddy, T. A., Girard, A., & Muneer, T. (2016). Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data. Renew. Energy, 95, 413–421.
Abstract: The performance of ground-source heat pumps (GSHP), often expressed as Power drawn and/or the COP, depends on several operating parameters. Manufacturers usually publish such data in tables for certain discrete values of the operating fluid temperatures and flow rates conditions. In actual applications, such as in dynamic simulations of heat pump system integrated to buildings, there is a need to determine equipment performance under operating conditions other than those listed. This paper describes a simplified methodology for predicting the performance of GSHPs using multiple regression (MR) models as applicable to manufacturer data. We find that fitting second-order MR models with eight statistically significant x-variables from 36 observations appropriately selected in the manufacturer catalogue can predict the system global behavior with good accuracy. For the three studied GSHPs, the external prediction error of the MR models identified following the methodology are 0.2%, 0.9% and 1% for heating capacity (HC) predictions and 2.6%, 4.9% and 3.2% for COP predictions. No correlation is found between residuals and the response, thus validating the models. The operational approach appears to be a reliable tool to be integrated in dynamic simulation codes, as the method is applicable to any GSHP catalogue data. (C) 2016 Elsevier Ltd. All rights reserved.
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Suresh, R., Alvarez, A., Sandoval, C., Ramirez, E., Santander, P., Mangalaraja, R. V., et al. (2023). Fe2O3/NiO nanocomposites: synthesis, characterization and roxarsone sensing by Fourier transform infrared photoacoustic spectroscopy. New J. Chem., 47(27), 12806–12815.
Abstract: The Fe2O3/NiO nanocomposite was prepared through facile mixing of pure Fe2O3 and NiO nanoparticles. Pure metal oxides were synthesized by the glycine aided hydrothermal method. The crystal structure was determined by X-ray diffraction (XRD) analysis. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) clearly show that the NiO nanospheres were homogeneously distributed on Fe2O3 nanoplates. Fourier transform infrared (FTIR) spectra reveal that functionalization by glycine induces carboxylic and amino groups on the surface of nanoparticles. The optical properties such as light absorption behavior and band gap of the Fe2O3/NiO nanocomposite were determined by UV-Visible diffuse reflectance spectroscopy (DRS UV-Vis). The roxarsone (ROX) sensing behavior of the Fe2O3/NiO nanocomposite was evaluated by using Fourier transform infrared (FTIR) – photoacoustic spectroscopy (PAS) that allowed quantitative sensing of the ROX@Fe2O3/NiO complex. The photoacoustic signals of ROX were clearly observed in the FTIR-PAS spectra, particularly in the range of 1700 to 1000 cm(-1) without spectral interference from the composite. Furthermore, principal component analysis (PCA) and partial least square (PLS) regression models were applied to calibrate and validate ROX quantification. The PLS model exhibited the best performance in predicting the ROX concentration through ROX@Fe2O3/NiO samples. This proof of concept suggests that the Fe2O3/NiO nanocomposite has improved adsorption and spectral features by FTIR-PAS for sensing of organometallic compounds such as ROX.
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Valdebenito, M. A., Wei, P. F., Song, J. W., Beer, M., & Broggi, M. (2021). Failure probability estimation of a class of series systems by multidomain Line Sampling. Reliab. Eng. Syst. Saf., 213, 107673.
Abstract: This contribution proposes an approach for the assessment of the failure probability associated with a particular class of series systems. The type of systems considered involves components whose response is linear with respect to a number of Gaussian random variables. Component failure occurs whenever this response exceeds prescribed deterministic thresholds. We propose multidomain Line Sampling as an extension of the classical Line Sampling to work with a large number of components at once. By taking advantage of the linearity of the performance functions involved, multidomain Line Sampling explores the interactions that occur between failure domains associated with individual components in order to produce an estimate of the failure probability. The performance and effectiveness of multidomain Line Sampling is illustrated by means of two test problems and an application example, indicating that this technique is amenable for treating problems comprising both a large number of random variables and a large number of components.
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Valle, M. A., Varas, S., & Ruz, G. A. (2012). Job performance prediction in a call center using a naive Bayes classifier. Expert Syst. Appl., 39(11), 9939–9945.
Abstract: This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the agent. (c) 2012 Elsevier Ltd. All rights reserved.
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Vera, R., Cruz, E., Bagnara, M., Araya, R., Henriquez, R., Diaz-Gomez, A., et al. (2018). Evaluation of anticorrosive coatings on carbon steel in marine environments: Accelerated corrosion test and field exposure. Int. J. Electrochem. Sci., 13(1), 898–914.
Abstract: This study assesses the behavior of two paint systems applied to A-36 steel, commonly used to cover industrial structures in marine environments. Accelerated tests were carried out in a salt spray chamber with a maximum of 3000 hours of exposure, while other tests were conducted in the field in five areas in Chile over a period of two years. Coatings were assessed with measurements of thickness, adherence, and blistering. The behaviors of these coatings were assessed using electrochemical impedance spectroscopy (EIS techniques, measuring the evolution of an impedance module at 0.1 Hz. The results show that, after two years of exposure or after 3000 hours in the salt spray chamber, the two coatings still present adequate protective properties, with an impedance module value log vertical bar Z vertical bar greater than 10(6) Omega cm(2). However, for all tests, comparing C5MB and C5IB coating systems, the latter is always less protective for the steel.
Keywords: ATMOSPHERIC CORROSION; SYSTEMS; PERFORMANCE
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Zhou, C. C., Zhang, H. L., Valdebenito, M. A., & Zhao, H. D. (2022). A general hierarchical ensemble-learning framework for structural reliability analysis. Reliab. Eng. Syst. Saf., 225, 108605.
Abstract: Existing ensemble-learning methods for reliability analysis are usually developed by combining ensemble learning with a learning function. A commonly used strategy is to construct the initial training set and the test set in advance. The training set is used to train the initial ensemble model, while the test set is adopted to allocate weight factors and check the convergence criterion. Reliability analysis focuses more on the local prediction accuracy near the limit state surface than the global prediction accuracy in the entire space. However, samples in the initial training set and the test set are generally randomly generated, which will result in the learning function failing to find the real ???best??? update samples and the allocation of weight factors may be suboptimal or even unreasonable. These two points have a detrimental impact on the overall performance of the ensemble model. Thus, we propose a general hierarchical ensemble-learning framework (ELF) for reliability analysis, which consists of two-layer models and three different phases. A novel method called CESM-ELF is proposed by embedding the classical ensemble of surrogate models (CESM) in the proposed ELF. Four examples are investigated to show that CESM-ELF outperforms CESM in prediction accuracy and is more efficient in some cases.
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