Background and Aim: ZOE has been used in different fields of dentistry for many years. A locally produced component (Zoliran) has been recently introduced to the marketwith similar characteristic to the original Zonalin. Because of a lower cost involved to use Zoliran cement and its availability, confirm reliability of its physical properties. This investigation was designed to assess the Compressive Strength of Zoliran cement in comparison to Zonalin cement as the standard material. Materials and Methods: Five samples with dimension of 4mmx6mm of each cement were provided and stored in distilled water in 370C±10C for a period of 24 hours. The lowest load of force was registered as the reference to which the sample could be broken by(according to the criteria No: 30 of ANSIIADA). The value of Compressive Strength was then calculated using the following formula (K =4F/πD2 ). Results: The mean Compressive Strength of five samples was measuredas: 14.33 Mpa for Zoliran cement and 31.83 Mpa for Zonalin cement. The mean Compressive Strength of Zonalincement was significantly higher than the mean suggested in ANSl/ADA Specification No.30. The mean CompressiveStrength of Zoliran cement was also lower than the mean value registered in ANSI/ADA SpecificationNo.30. Conclusion: Compressive Strength of Zoliran cement was significantlylower than that of Zonalin cement. Further tests are required to compare the other physical properties of this material before it can be clinically recommended.

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.

Accurate Prediction of the uniaxial Compressive Strength (UCS) of concrete is crucial for ensuring the safety, durability, and performance of structures in construction. This study presents a predictive model using a multilayer perceptron (MLP), to estimate UCS based on key input parameters such as water-cement ratio, aggregate size, curing time, water and cement content. The MLP model was trained and validated using a dataset comprising 120 cubic laboratory-tested concrete samples (15cm × 15cm × 15cm) with varying compositions for normal construction materials. Performance of the model was evaluated using statistical metrics (split into training and testing sets as 70%-30%), showing that the MLP-based approach provides accurate and reliable Predictions compared to traditional regression models. The proposed method offers a practical, efficient tool for geotechnical engineers to assess concrete Strength, potentially reducing the need for extensive experimental testing and enhancing quality control in concrete production.

The effect of resin on treatment of soil-cement was studied through experimental tests.Three kinds of commercial resins with different percentage weights were used within the study. Test samples were made of mixtures of soil with different percentage weights of cement (8 % and 12%) and mixing of soil-cement with different percentage contents of resin (5, 8 and 10%). Unconfined compression tests were conducted on the prepared samples at different curing times. Results indicated that adding resin to soil-cement causes increase in the Strength of the mixture. In addition, an increase in the Strength is a function of percent cement content, resin percentage, type of resin (viscosity of the resin) as well as curing time. A regression model was proposed as based on the experimental data for predicting the Compressive Strength. The regression model consisted of percentage content cement percent resin, kind of resin (resin viscosity) as well as curing time as variables. A comparison between the model Predictions and the experimental results reveals that the proposed models can satisfactorily predict the Compressive Strength as regards soil-cement resin mixtures.

Geopolymers represent a cutting-edge class of inorganic materials that provide a sustainable substitute for conventional cement and concrete. Through meticulous combinations and ratios of elements like Fly Ash (FA), silica fume, Ground Granulated Blast Slag (GGBS), alkaline solutions, aggregates, superplasticizers, and fibers, geopolymer concrete mixes are generated as part of the experimental program. The investigation concentrates on predicting the 28-day Compressive Strength, a pivotal parameter in assessing concrete performance. The dataset comprises 96 data points, and two advanced techniques, namely Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are harnessed for this research. The ANN demonstrates an  value of 0.992 on the training dataset, indicating its capacity to elucidate around 99.2% of the variability. On the other hand, SVR boasts an  value of 0.995, signifying an ability to account for about 99.5% of the variance. When applied to the testing data, the ANN achieves an  of 0.96, while SVR attains an  of 0.99. This study suggests that SVR exhibits slightly superior performance in elucidating variance within the testing dataset.

Fiber Reinforced Polymer (FRP) was extensively employed as external confinement to Strengthen the RC structures. Extensive studies were carried out to assess a more exact formula for measuring the Strength enhancement of such Strengthens concrete columns. A database from several experimental tests on was gathered. A comparison between the experimental values and existing formulae showed an urgent need for a more exact formula. This study investigated to develop an exact formula based artificial neural networks (ANNs), to present the Strength enhancement. The ANN-based method was simulated based on the collected database and an exact formula generated. The proposed formula was compared to current formulae employing the gathered database. The results demonstrated that the new formula based ANN gives the best accuracy than others. A sensitivity analysis based on Garson’ s algorithm was generated for indicating the value of each used variable.

A non-recyclable material that enters the environment is used car tires. Research shows that used tires are made of materials that, due to their non-decomposition under normal conditions, cause pollution and damage to the environment. According to research, one method of removing these materials is to use rubber waste in concrete. Therefore, in this study, aggregate composites were replaced by waste rubber particles the Compressive Strength of concrete was estimated by artificial neural network using the input parameters water to cement ratio, superplasticizer additive and granulation weight composition. The results of this study were compared with other related research studies and confirmed the superiority and high accuracy of the artificial neural network obtained in this study. The a-20 engineering index of the neural network was determined to be one and the error of 99% of the data was less than 15%, indicating the appropriate approximation of the Compressive Strength of concrete containing waste rubber particles by the artificial neural network. In addition, the results of the sensitivity analysis using the Millen method indicated a 40% effect of the weight of the superplasticizer additive as a sensitive parameter in this type of concrete.

Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, artificial neural networks (ANN) for predicting Compressive Strength of cubes and durability of concrete containing metakaolin with fly ash and silica fume with fly ash are developed at the age of 3, 7, 28, 56 and 90 days. For building these models, training and testing using the available experimental results for 140 specimens produced with 7 different mixture proportions are used. The data used in the multi-layer feed forward neural networks models are designed in a format of eight input parameters covering the age of specimen, cement, metakaolin (MK), fly ash (FA), water, sand, aggregate and superplasticizer and in another set of specimen which contain SF instead of MK. According to these input parameters, in the multi-layer feed forward neural networks models are used to predict the Compressive Strength and durability values of concrete. It shown that neural networks have high potential for predicting the Compressive Strength and durability values of the concretes containing metakaolin, silica fume and fly ash.

In this paper, generalized Group Method of Data Handling (GMDH)-type neural network has been successfully used for modeling concrete core testing including reinforcing bars based on various data obtained experimentally. Genetic Algorithm (GA) and Singular Value Decomposition (SVD) techniques are deployed for optimal design of such model. A set of input-output data for the training and testing the evolved models are employed in which core diameter, length-to-diameter ratio, number of reinforcing bars, distance of bar axis from nearer end of core as well as Strength of cores, with or without reinforcing bars, are considered as inputs and standard cube Strength of concrete is regarded as the output variables. The comparison of results obtained experimentally in this work with the proposed GMDH model depicted that this model has a great ability for Prediction of the concrete Compressive Strength on the basis of core testing. Finally, sensitivity analysis was performed on the models obtained by GMDH-type neural network to study the influence of input parameters on model output. The sensitivity analysis reveals that the output variable (standard cube Strength) is significantly changed by core Strength and number of rebars in comparison with other input variables.

In this research, a fuzzy system has been presented to predict the behavior of self-consolidated concrete (SCC) under tidal conditions in Sea of Oman. As the coastal concrete structures are subjected to chloride attack, it is highly essential to consider their Compressive Strength. For this purpose, in order to define the fuzzy membership functions, a number of SCC specimens of different w/c ratios, different percentages of micro-sio2 and nano-sio2 were made and placed on the coast of the Sea of Oman at different ages. These concrete specimens were covered with nets and subjected to sea tidal condition. After that, a fuzzy system was defined which includes four input functions: w/c ratio, age, amount of micro-sio2 and nano-sio2. The output function was the Compressive Strength of the concrete. In this fuzzy system, Mamdani inference engine was used. Ultimately, this system was able to predict the Compressive Strength of self-compacting concrete under tidal condition with high accuracy and with only an 3% difference.