Prediction and optimization of hardened properties of concrete prepared with granite dust and scrapped copper wire using response surface methodology.

Urban growth in the developing world has prompted researchers to seek alternatives to fine aggregate due to the severe environmental impact of extensive natural sand depletion. On top of that, the accumulation of non-biodegradable dumps, solid trash such as scrapped copper wire (SCW), and industrial remnants like granite dust (GD) have reached alarming levels. Therefore, incorporating these two waste materials in concrete offers a potentially sustainable solution. The study aims at substituting natural fine aggregate with GD as well as incorporating SCW for predicting and optimizing the compressive and splitting tensile strength of concrete using response surface methodology (RSM). Two independent variables, the volumetric percentages of GD (10 %, 20 %, and 30 %) and SCW (0.1 %, 0.3 %, and 0.5 %) in a concrete mix ratio of 1:1.5:3, were utilized to create probabilistic models for compressive and splitting tensile strength at 7 and 28 days. The experimental design employed central composite design (CCD) of RSM and the results of both ANOVA and regression analysis in terms of several statistical functions demonstrated a strong correlation between the predicted values of the responses and the actual experimental results. The developed models were validated by conducting experiments using optimized proportions of GD (23.32 %) and SCW (0.37 %). Finally, the strengths of the optimum content mix yielding 25.12 MPa and 3.266 MPa, respectively for compressive and splitting tensile at 28 days ensure the efficiency of the models due to the substantial similarity between experimental and predicted values. Therefore, integrating GD and SCW for higher-strength concrete in mass production can be a cost-effective alternative, fostering increased recycling of waste and supporting sustainable growth in building construction.

Talking Trials: An arts-based exploration of attitudes to clinical trials amongst minority ethnic members of the South Riverside Community of Cardiff.

INTRODUCTION: Clinical trials must include diverse participants to ensure the wide applicability of results. However, people from ethnic minorities are included in clinical trials at rates lower than expected given their share of the population. Working with South Riverside Community Development Centre (SRCDC), Talking Trials used public engagement to foster discussions around the underrepresentation of those from minority ethnic communities in clinical trials and to identify and address concerns surrounding trial participation. METHODS: We conducted three workshops with 13 co-researchers from minority ethnic backgrounds. We explored perceptions and understanding of clinical trials alongside participatory art activities to help move away from verbocentric methods of communication. These artworks formed an exhibition that was presented to the community, prompting further discussions and engagement. FINDINGS: Co-production workshops were an effective tool to introduce the public to trial research. With little knowledge of clinical trials at the beginning of the process, our co-researchers formed a cohesive group, sharing initial fears and mistrust towards trials. As conversations progressed these attitudes clearly shifted. Artwork produced during the workshops was incorporated into an exhibition. Quotes and creative pieces from the group were included to reflect the themes identified. Presenting the exhibition at Riverside Festival enabled further engagement with a wider diverse community. The focus on co-production helped build a network of individuals new to research and keen to become involved further. CONCLUSION: Inclusive and democratic co-production, enriched by participatory art practices, provided a powerful means of enabling our group to create new insights and foster new relationships. Projects like Talking Trials can diversify the research process itself-for example, four co-researchers have commenced lay research partner roles on trial management groups and a lay advisory group is in development. PATIENT OR PUBLIC CONTRIBUTION: Three members of staff at SRCDC were on the project delivery group and involved in the initial project design, subsequently helping to connect us with members of the Riverside community to work as co-researchers. Two of the SRCDC staff are co-authors of this manuscript. The project had 13 public co-researchers guiding the direction of this research and creating the artwork displayed in the art exhibition.

Prediction and optimization of properties of concrete containing crushed stone dust and nylon fiber using response surface methodology.

Over-extraction of aggregates from natural sources with rapid urbanization as well as massive waste generation in construction industry have imposed the need to utilize waste material as concrete constituent. Crushed Stone Dust (CSD) is such a supplementary material that can be utilized for the production of sustainable concrete. This study attempts to predict and optimize fresh and hardened properties of concrete utilizing CSD as a partial replacement of natural fine aggregate and Nylon Fiber (NF) as fiber reinforcement using Response Surface Methodology (RSM). A three-level factorial design of Box-Behnken was incorporated to investigate the effect of CSD, NF and W/C as three independent variables on compressive strength, splitting tensile strength, fresh density and workability of concrete as desired responses. All the developed probabilistic models were found to be significant in predicting the responses at 95% confidence level. Regression analysis in terms of correlation coefficient, coefficient of determination, coefficient of variation, adequate precision, chi-square, mean square error, root mean square error, and mean absolute error also indicated the accuracy and functionality of the developed models. The results reveal that both compressive and splitting tensile strength increase with increased NF content, but the rise in CSD percentages beyond a certain level has negative impact on strength of concrete. However, fresh density and workability of concrete show a declining trend with rise in both CSD and NF levels. From multi-objective optimization, 20% CSD, 0.75% NF and W/C of 0.49 have been found to be the optimum proportions for concrete mixture with a desirability of 0.915. Finally, an experimental validation was carried out with optimum mixture contents and relative error between the experimental and predicted optimized values was observed to be less than 5%.

Predicting the strength of concrete made with stone dust and nylon fiber using artificial neural network.

Excessive demand of concrete is causing depletion of natural sand resources. Especially, the extraction of river sand negatively affects its surrounding environment. A sustainable solution to this problem can be the proper utilization of waste materials and by-products like stone dust (SD) as fine aggregate replacement in concrete. The recycling of stone dust as a construction material lessens the use of natural resources and helps to solve landfill scarcity as well as environmental problems. Addition of nylon fiber (NF) as fiber reinforcement can also attribute to enhance the properties of concrete. This research aims at utilizing SD as fine aggregate along with NF, and assessing the compressive strength and splitting tensile strength of concrete. Although the individual effects of incorporating stone dust and nylon fiber in concrete have been investigated in previous researches, their combined effects, as well as effects of water cement (WC) ratio on concrete strength, have not been studied yet. In this study, volumetric percentages of stone dust (20%-50%) and nylon fiber (0.25%-0.75%) and three different water cement ratio (0.45, 0.50 and 0.55) have been considered as three independent variables to develop probabilistic models for compressive strength and splitting tensile strength of concrete using artificial neural network (ANN). The values of coefficient of determination (R2) and other statistical parameters of the developed probabilistic models indicate the accuracy of the models to predict the concrete strength. In terms of compressive strength at early age, the optimal percentages of SD and NF have been found as 20% and 0.25%, respectively. However, the strength gradually drops as water cement ratio elevates from 0.45 to 0.55. The reduction of the splitting tensile strength has been observed for increasing SD from 20% to 50%, whereas, strength increases for rising NF and WC up to mid-level.