Structural Performance of GFRP Bars based High-Strength RC Columns: An Application of Advanced Decision-Making Mechanism for Experimental Profile Data

Several past studies have shown the use of glass fibre-reinforced polymer (GFRP) bars to alleviate the reinforced steel rusting issue in different concrete structures. However, the practise of GFRP bars in concrete columns has not yet achieved a sufficient confidence level due to the lack of a theoretical model found in the literature. The objective of the current study is to introduce a novel prediction model for the axial capability of concrete columns made with bars of GFRP. For this purpose, two different approaches, such as data envelopment analysis (DEA) and artificial neural networks (ANNs) modelling, are used on a collected dataset of 266 concrete column specimens made with GFRP bars from previous literature works. Eight parameters were used to predict the axial performance of GFRP-based RC columns. The proposed DEA and ANNs predictions demonstrated a good correlation with the testing dataset, having R2 values of 0.811 and 0.836, respectively. A comparative analysis of the DEA and ANNs models is undertaken, and it was found that the suggested models are capable of accurately forecasting the structural response of GFRP-made RC column structures. Then, a comprehensive parametric analysis of 266 GFRP-based columns was performed to study the effect of different materials and their geometrical shape.publishedVersio

Optimizing the Flexural Behavior of Bamboo Reinforced Concrete Beams Containing Cassava Peel Ash using Response Surface Methodology

The growing concern to reduce global warming has necessitated the use of more eco-friendly materials in construction. The study is focused on the utilization of cassava peel ash as supplementary cementitious material and bamboo as reinforcement in concrete beams. The response surface methodology approach was explored to determine the effect of simultaneously varying the cassava peel ash content, bamboo size, beam length, and beam depth on the flexural strength and strain of beams. An analysis of variance was carried out on experimentally obtained results to determine the accuracy of the obtained models and the contributions made by the linear interaction and quadratic terms on flexural strength and flexural strain. The coefficient of determination obtained for RSM models showed a good correlation between all predicted and experimentally obtained results. The optimum conditions obtained for bamboo-reinforced concrete containing cassava peel ash were 3% cassava peel ash, 16 mm bamboo diameter, 500 mm beam length, and 150 mm beam depth. The predicted flexural strengths were 11.85, 14.34, and 14.95 N/mm2 and flexural strains of 0.64, 0.67, and 0.91 for 28 days, 56 days, and 90 days, respectively. To validate the model prediction, a laboratory experiment was conducted using the optimum mix design proportion. From the results obtained, it was observed that the experimental results were close to those predicted by the models. These models can be efficiently used for simulating the flexural behavior of bamboo-reinforced concrete beams. Doi: 10.28991/CEJ-2023-09-08-011 Full Text: PD

Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures

High-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting the compressive strength of HSC under high-temperature conditions is crucial for safety. Machine learning (ML) techniques have emerged as a powerful tool for predicting concrete properties. Accurate prediction of the compressive strength of HSC is important as HSC can experience strength losses of up to 80% after exposure to temperatures of 800°C–1000°C. This study evaluates the efficacy of ML techniques such as Extreme Gradient Boosting, Random Forest (RF), and Adaptive Boosting for predicting the compressive strength of HSC. The results of this study demonstrate that the RF model is the most efficient for predicting the compressive strength of HSC, exhibiting the R2 value of 0.98 and lower mean absolute error and root mean square error values than the other applied models. Furthermore, Shapley Additive Explanations analysis highlights temperature as the most significant factor influencing the compressive strength of HSC. This article provides valuable insights into the timely and effective determination of the compressive strength of HSC under high-temperature conditions, benefiting both the construction industry and academia. By leveraging ML techniques and considering the critical factors that influence the compressive strength of HSC, it is possible to optimize the design and construction process of HSC and enhance its resilience to high-temperature exposure

Mechanical Performance of Polymeric ARGF-Based Fly Ash-Concrete Composites: A Study for Eco-Friendly Circular Economy Application

At present, low tensile mechanical properties and a high carbon footprint are considered the chief drawbacks of plain cement concrete (PCC). At the same time, the combination of supplementary cementitious material (SCM) and reinforcement of fiber filaments is an innovative and eco-friendly approach to overcome the tensile and environmental drawbacks of plain cement concrete (PCC). The combined and individual effect of fly ash (FA) and Alkali resistance glass fiber (ARGF) with several contents on the mechanical characteristics of M20 grade plain cement concrete was investigated in this study. A total of 20 concrete mix proportions were prepared with numerous contents of FA (i.e., 0, 10, 20, 30 and 40%) and ARGF (i.e., 0, 0.5, 1 and 1.5%). The curing of these concrete specimens was carried out for 7 and 28 days. For the analysis of concrete mechanical characteristics, the following flexural, split tensile, and compressive strength tests were applied to these casted specimens. The outcomes reveal that the mechanical properties increase with the addition of fibers and decrease at 30 and 40% replacement of cement with fly ash. Replacement of cement at higher percentages (i.e., 30 and 40) negatively affects the mechanical properties of concrete. On the other hand, the addition of fibers positively enhanced the flexural and tensile strength of concrete mixes with and without FA in contrast to compressive strength. In the end, it was concluded that the combined addition of these two materials enhances the strength and toughness of plain cement concrete, supportive of the application of an eco-friendly circular economy. The relationship among the mechanical properties of fiber-reinforced concrete was successfully generated at each percentage of fly ash. The R-square for general relationships varied from (0.48–0.90) to (0.68–0.96) for each percentage of FA fiber reinforced concrete. Additionally, the accumulation of fibers effectively boosts the mechanical properties of all concrete mixes.publishedVersio

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)