Abstract: There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.
Abstract: Response Surface Methods (RSM) provide
statistically validated predictive models that can then be manipulated
for finding optimal process configurations. Variation transmitted to
responses from poorly controlled process factors can be accounted
for by the mathematical technique of propagation of error (POE),
which facilitates ‘finding the flats’ on the surfaces generated by
RSM. The dual response approach to RSM captures the standard
deviation of the output as well as the average. It accounts for
unknown sources of variation. Dual response plus propagation of
error (POE) provides a more useful model of overall response
variation. In our case, we implemented this technique in predicting
compressive strength of concrete of 28 days in age. Since 28 days is
quite time consuming, while it is important to ensure the quality
control process. This paper investigates the potential of using design
of experiments (DOE-RSM) to predict the compressive strength of
concrete at 28th day. Data used for this study was carried out from
experiment schemes at university of Benghazi, civil engineering
department. A total of 114 sets of data were implemented. ACI mix
design method was utilized for the mix design. No admixtures were
used, only the main concrete mix constituents such as cement, coarseaggregate,
fine aggregate and water were utilized in all mixes.
Different mix proportions of the ingredients and different water
cement ratio were used. The proposed mathematical models are
capable of predicting the required concrete compressive strength of
concrete from early ages.