Nowadays, the better performance of lightweight structures during earthquake has resulted in using lightweight concrete more than ever. However, determining the Compressive strength of concrete used in these structures during their service through a none-destructive test is a popular and useful method. One of the most original approach of non-destructive testing to obtain of Compressive strength of concrete used in structures is ultrasonic pulse velocity test. The purpose of this research is predicting the Compressive strength of LWA concrete by proposing an accurate mathematical formulation. Many samples of lightweight aggregate concrete, made by expanded clay, have been produced and tested. After determining the actual Compressive strength and indirect ultrasonic pulse velocity for each sample, a relationship was derived to estimate the Compressive strength through Gene Expression Programming (GEP). The results show the presented equation shows high accuracy in predicting the Compressive strength of LWA and the estimated outcomes have a considerable compatibility with actual samples.

This paper presents a new model for predicting the Compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) using a wide range of experimental investigations. Based on the previous theoretical and experimental studies the input variables considered in developing the ANNs model are outer diameter of column, Compressive strength of unconfined concrete, length of column, wall thickness and tensile yield stress of steel tube. After the learning step, the neural network can be extracted the relationships between the input variables and output parameter. The criteria for stopping the training of the networks are Regression values and Mean Square Error. After constructing networks with constant input neurons but with different number of hidden-layer neurons, the best network was selected. The neural network results are compared with the existing models which showed the results are in good agreement with experiments.

The development of alternate material to replace cement by new eco-friendly materials such as fly ash, rice husk ash, silica fume slag etc., by method of alkali activation under thermal curing process forms a new binder called geopolymer. These materials not only increases the strength but considerably decreases the emission of carbon dioxide gas to the atmosphere and also these mineral admixtures also can be used as a replacement material for cement to meet the future demand. In this study an attempt has been made to produce ground granulated blast furnace slag (GGBFS) based geopolymer concrete and to find out its Compressive strength characteristics by considering the parameters such as ratio of alkaline liquid (AL) to (GGBFS), ratio of silicate to hydroxide (SiO3/OH) and the age of geopolymer concrete with different temperatures of thermal curing has been adopted. Detailed experimental investigations were carried out to assess the effect of the above mentioned parameters on Compressive strength. From the results it was understood that with AL/GGBFS = 0.30, SiO3/OH= 1.5, for thermal curing temperature of 100oC, for curing period of 56 days yielded better Compressive strength when compared to conventional concrete under normal curing. Hence use of GGBFS based geopolymer concrete is recommended for construction.

The present work is aimed at studying the effect of re-vibration on the Compressive strength of concrete using 53 grade Ordinary Portland Cement, with wide range of w/c ratios varying from 0.35 to 0.7 and with more number of re-vibration time lag intervals ranging from ½ hour to four hours. The effect of re-vibration on the density of concrete is also studied. The results have shown that the Compressive strength of the concrete with various w/c ratios have increased up to certain time lag interval i.e. within the initial setting time (IST) and decreased thereafter. The percentage increase in the Compressive strength also nearly followed the above trend.

This study introduces, two models based on Gene Expression Programming (GEP) to predict Compressive strength of high strength concrete (HSC). Composition of HSC was assumed simplified, as a mixture of six components (cement, silica fume, super-plastisizer, water, fine aggregate and coarse aggregate). The 28-day Compressive strength value was considered the target of the prediction. Data on 159 mixes were taken from various publications. The system was trained based on 80% training pairs chosen randomly from the data set and then tested using remaining 20% samples. Therefore it can be proven and illustrated that the GEP is a strong technique for the prediction of Compressive strength amounts of HSC concerning to the outcomes of the training and testing phases compared with experimental outcomes illustrate that the.

Alkali Activated Slag (AAS) concrete is a combination of blast furnace slag and alkaline solution that is used as a binder. In this paper, Taguchi method was used to design of AAS mixtures. By considering four affecting factors including concentration of sodium hydroxide (NaOH) solution, sodium hydroxide to sodium silicate ratio, alkaline solution to slag ratio, and aggregate content, the optimal mixture design that provides the maximum Compressive strength and flexural strength was identified. To verify the results, the optimal mixture was produced and tested. The mixture resulted in more Compressive strength and flexural strength than other mixes.