Estimation Of Compressive Strength Research Articles

DETERMINATION OF BUILDINGS WITH TORSIONAL IRREGULARITY BY ARTIFICIAL INTELLIGENCE METHODS

Reinforced Concrete (RC) frame buildings with shear wall are widely used in severe seismic zones. Shear walls are bearing system elements that provide the greatest resistance against hori-zontal force under the effect of the earthquake, limit displacements, and prevent torsions. A re-inforced concrete shear wall is one of the most critical structural members in buildings, in terms of carrying lateral loads. However, irregular layouts cause torsional irregularity in buildings. For this purpose, different shear wall frame reinforced concrete building models are designed. The model buildings have a regular formwork plan. The shear wall layout has different variations in each plan. These structure plans were mainly classified into two classes according to their tor-sional irregularities as structures with torsional irregularities and Structures with non-torsional ir-regularities. Artificial intelligence (AI) has revolutionized industries such as healthcare, agricul-ture, transportation, and education, as well as a variety of structural engineering problems. Arti-ficial intelligence is transforming decision-making easier and reshaping building design process-es to be smarter and automated. Artificial intelligence technology of learning from an existing knowledge base is used to automate various civil engineering applications such as compressive strength estimation of concrete, project pre-cost and duration, structural health monitoring, crack detection, and more. In this study, it is aimed to determine the structures with torsional irregulari-ty using artificial intelligence methods. Besides, the study is expected to introduce and demon-strate the capability of Artificial intelligence-based frameworks for future relevant studies within structural engineering applications and irregularities.

Prediction of compressive strength of masonry structures: Integrating three optimized models by virtue of committee machine

An accurate computation of the compressive strength of masonry structures is an overarching factor in design and construction of masonry structures. This considerable significance compels researchers to propose an appropriate, reliable and, more generalized method whereby the precise value of compressive strength of masonry structures is calculated. In the current study, a committee machine with optimized elements is constructed, thereby extracting a non-linear relationship between compressive strength of masonry structures with compressive strength of mortar and brick. In order to accomplish this objective, three intelligent models viz. neural network, fuzzy inference system, and support vector regression are firstly optimized with bat-inspired algorithm, and these improved models are subsequently applied for estimation of compressive strength of masonry structures. bat-inspired algorithm is hybridized with intelligent models for extracting the best values of weights and biases of neural network, membership’s functions of fuzzy inference system, and user-defined parameters of support vector regression. Then, committee machine is utilized for amalgamating the outputs of three optimized models incl. optimized neural network, optimized fuzzy inference system, and optimized support vector regression. bat-inspired algorithm is also embedded in the structure of committee machine, thereby determining the optimal contribution of each optimized model in the final prediction. Data sets including 96 records of accessible in the literature are used to learn and evaluate the constructed models. Appraisal of the accuracy based on statistical parameters verified that the committee machine could effectively improve the prediction accuracy of the optimized models and also has a better performance compared to commonly well-known predictive correlations. This study also proved that committee machine with optimized elements is a very convenient approach for mapping nonlinear functions between compressive strength of masonry structures and compressive strength of brick and mortar.

Study Regarding the Evaluation of Prediction Models for Determining the Concrete Compressive Strength Using Non-Destructive Testing (NDT) Data: Validation Stage

In the evaluation of an existing reinforced concrete structure, a fundamental issue is determining the concrete compressive strength as accurately as possible. This process can be conducted by using destructive and non-destructive methods. The destructive method presumes a limited number of cores extracted from the concrete structure. A higher number of cores would affect the structural safety and is time and resource consuming. Therefore, the conclusions drawn using exclusively this method can also generate errors in correctly estimating the load bearing capacity of a structure, thus leading to the possibility of implementing deficient measures in order to ensure a structural safety. Data obtained via non-destructive methods are more comprehensive. Due to their non-destructive nature, there are no limitations regarding the number of elements investigated and are fast in delivering results. One of the main concerns of researchers in this field is developing a direct relationship between the measured indicators through non-destructive testing (NDT) methods and concrete compressive strength. Over the years different equations with different mathematical structure (linear, polynomial, power, exponential and logarithmic) were developed with the main purpose of delivering fast and accurate results concerning concrete compressive strength by the means of NDT. The aim of this paper is to validate some of the most important prediction models using an original set of data. The database consists in a number of 96 concrete cores that were subjected to Ultrasonic Pulse Velocity (UPV) and Schmidt Rebound Hammer (SRH) testing. The accuracy of the results was determined by using two statistical parameters the mean absolute error (MAE) and mean absolute percentage error (MAPE). The proposed equations have been analyzed in terms of prediction and dispersion of values. It was noticed that some of the formulations predict values that are higher than the ones obtained destructively, others provide a larger dissipation of values, while some equations deliver a compact distribution of results with higher rate in terms of accuracy. This study proposes a data validation of some of the most popular empirical equations, used for the estimation of the concrete compressive strength, elaborated through the years, using a new set of data.

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models.

Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.

The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na2SiO3/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R2). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.

Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete.

Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variables, such as the water-cement ratio, the strength of the parent concrete, recycled aggregate replacement ratio, density, and water absorption of recycled aggregate, all impact the RAC’s CS. Many studies have been carried out to ascertain the influence of each of these elements separately. However, it is difficult to investigate their combined effect on the CS of RAC experimentally. Experimental investigations entail casting, curing, and testing samples, which require considerable work, expense, and time. It is vital to adopt novel methods to the stated aim in order to conduct research quickly and efficiently. The CS of RAC was predicted in this research utilizing machine learning techniques like decision tree, gradient boosting, and bagging regressor. The data set included eight input variables, and their effect on the CS of RAC was evaluated. Coefficient correlation (R2), the variance between predicted and experimental outcomes, statistical checks, and k-fold evaluations, were carried out to validate and compare the models. With an R2 of 0.92, the bagging regressor technique surpassed the decision tree and gradient boosting in predicting the strength of RAC. The statistical assessments also validated the superior accuracy of the bagging regressor model, yielding lower error values like mean absolute error (MAE) and root mean square error (RMSE). MAE and RMSE values for the bagging model were 4.258 and 5.693, respectively, which were lower than the other techniques employed, i.e., gradient boosting (MAE = 4.956 and RMSE = 7.046) and decision tree (MAE = 6.389 and RMSE = 8.952). Hence, the bagging regressor is the best suitable technique to predict the CS of RAC.

Evaluation and Estimation of Compressive Strength of Concrete Using Hybrid Modeling Techniques

Evaluation and Estimation of Compressive Strength of Concrete Using Hybrid Modeling Techniques

Estimation of uniaxial compressive strength and intrinsic permeability from ultrasounds in sedimentary stones used as heritage building materials

This paper combines ultrasonic parameters recorded from P- and S- waves and effective porosity to estimate uniaxial compressive strength, σC, and intrinsic permeability, k - two of the most crucial physical parameters used in stone conservation studies. The ultrasonic parameters have been chosen because they are sensitive to the microstructural properties of stones which influence both σC and k differently. Velocities of compressional or primary, Vp, and shear or secondary, Vs, elastic waves are related to porosity and the mineralogical composition; the waveform attenuation is related to the presence of vuggy macropores and pore and particle size; and the wavelength is related to the pore and particle size. Fifteen types of stones used in the Spanish built heritage were measured including calcarenites, sandstones, dolostones and travertines. Several simple and multiple predictive expressions for σC and k are fitted, which are quantified through the Pearson correlation coefficient, R. Curve fitting improves as the number of petrophysical parameters taken into account in the multiple linear regressions increases. The obtained Pearson's correlations values for σC are slightly higher than for k. The best correlation for σC (R = 0.921) and k (R = 0.903) is found when the predictive equation incorporates all of the elastic wave parameters obtained non-destructively, and the connected porosity. In general, effective porosity is statistically the most significant variable of the fitted regressions. The σC and k estimation from all the ultrasonic parameters, without considering connected porosity, shows a good correlation (R = 0.852 and 0.864, respectively). The addition of Vs to multiple regressions does not significantly improve the goodness of the fit. Consequently, the measurement of Vs is unnecessary as it is difficult and time-consuming. Finally, results also show that vuggy porosity (present in travertines) influences waveform attenuation and σC and k differently, relative to other microstructural properties; limiting the techniques use for stones with this type of porosity. Estimating σC and k using ultrasonic parameters exclusively is advantageous as the estimations can be obtained non-destructively using portable equipment, while also providing an acceptable goodness of fit.

Comparison of different machine learning methods for estimating compressive strength of mortars

Compressive strength and ultrasonic pulse velocity (UPV) are two of the most preferred technics for determining both mechanical properties and microstructural characteristics of cement-based materials. Since the most important mechanical property of concrete compared to many other properties is its compressive strength, it is of great importance to predict and determine the compressive strength. In the current investigation, the compressive strength and ultrasonic pulse velocities of twelve (12) different cement mortars containing different amounts of fly ash and nano calcite were experimentally obtained for the curing ages of 1, 3, 7, 28 and 90 days. By using the experimentally obtained data, compressive strength was estimated by the regression methods developed using the extreme learning machine (ELM), support vector machine (SVM) and group method of data handling (GMDH). In order to compare both experimental and predicted results and also to measure their performance, coefficient of determination (R2) and mean squared error (MSE) were utilized. Two different methods were made for each regression method. These were; Method 1: Estimation of compressive strength values without including UPV test results and Method 2: Estimation of compressive strength values including UPV results. Thus, the relationship between (i) the compressive strength and UPV experimental results and (ii) the estimation results of the regression models were determined and then compared. In this study, the best test performances (namely, for R2/MSE) were found as 0.917/20.239 with the ELM model in the Method 1; 0.982/3.882 with the SVM model in the Method 2. According to these results, it was revealed that the selected regression models achieved high success in the estimation of compressive strength.

Discussion on the paper entitled “Estimating elasticity modulus and uniaxial compressive strength of sandstone using indentation test” by E. Mousavi, A. Cheshomi and M. Ashtari [Journal of Petroleum Science and Engineering 169 (2018) 157–166

A discussion on the paper by Mousavi et al. (2018) is presented. The paper is focused on the estimation of elastic modulus (E) and uniaxial compressive strength (UCS) of sandstone using indentation test. The authors concluded that the thickness effect of samples on the indentation indices (critical transition force (CTF) and indentation modulus (IM)) was negligible. They proposed a dimensionless surface parameter (S) to eliminate the size effect on the E and UCS. On the basis of the test results and analysis method presented in the paper by Mousavi et al. (2018), three aspects of indentation test need to be clarified: the methods to prevent the movement of rock specimens, the results obtained from indentation test and the effect of specimen sizes on the CTF and IM. Meanwhile, the present discussion paper proposed some references for the methods to prevent the movement of rock specimens and the methods to eliminate the size effect in indentation test.

Advanced Estimation of Compressive Strength and Fracture Behavior in Ceramic Honeycombs by Polarimetry Measurements of Similar Epoxy Resin Honeycombs.

Finding a non-destructive characterization method for cellular ceramics’ compressive strength and fracture behavior has been a challenge for material scientists for years. However, for transparent materials, internal stresses can be determined by the non-destructive photoelastic measurements. We propose a novel approach to correlate the photoelastic stresses of polymer (epoxy resin) prototypes with the mechanical properties of ceramics (alumina). Regular and inverse epoxy honeycombs were 3D-printed via stereolithography with varying structure angles from −35° to 35°, with negative angles forming an auxetic and positive hexagonal lattice. Photoelastic measurements under mechanical loading revealed regions of excess stress, which directly corresponded to the initial fracture points of the ceramic honeycombs. These honeycombs were made by a combination of 3D printing and transfer molding from alumina. The photoelastic stress distribution was much more homogeneous for angles of a smaller magnitude, which led to highly increased compressive strengths of up to 446 ± 156 MPa at 0°. By adapting the geometric structural model from Gibson and Ashby, we showed that we could use a non-destructive technique to determine the compressive strength of alumina honeycombs from the median photoelastic stress measured on similar epoxy honeycomb structures.

Influence of Drilling Methods on the Results of Standard Penetration Test in Loess–Paleosol Sequence

Standard penetration test (SPT) is an important in situ measurement for field investigation of geotechnical and geological engineering. The drilling approaches for implementation of SPT can be classified into dry, wet, and water circulation drillings according to the amount of water used during drilling process. However, the influences of these drilling methods on the SPT results remain unclear, especially when being used in loess–paleosol sequence that is water sensitive. In this study, SPT tests were conducted in a typical loess stratum in the Loess Plateau of China. The difference of SPT N values under the above three drilling methods was compared together with the analysis of characteristics of samples from SPT sampler. The results showed that the N value exhibits positive correlation with dry density of the soil and negative correlation with moisture content. In shallow soil, the average N value under water circulation drilling was slightly higher than that of dry and wet drilling. This is because that the residual soil at the bottom of the drillhole caused by water circulation drilling provides additional penetration resistance. In deep soil, the difference of average N values among all three drilling methods was minimal although the structure of the samples from the SPT sampler differs from one another, indicating the determination of soil density on the SPT result. Empirical equations were proposed for the estimation of unconfined compressive strength of loess–paleosol sequence on the basis of SPT N values under the three drilling methods. Considering the efficiency of drilling and stability of SPT results, it is suggested that wet drilling is the most applicable method for implementation of SPT in the field investigation of loess–paleosol sequence.

Compressive Strength Estimation and CO2 Reduction Design of Fly Ash Composite Concrete

Fly ash is broadly utilized to produce concrete materials. This study presents a strength estimation model and a CO2 reduction design method for concrete with fly ash. First, a hydration-based strength (HBS) model is proposed for the evaluation of strength development at different ages of fly ash composite concrete with different mix proportions. Second, CO2 emissions for 1 MPa strength were evaluated. The analysis results show that, as the fly ash-to-binder ratio (FA/B) increased, the CO2 emissions for 1 MPa strength decreased. For concrete with a low water-to-binder ratio (W/B), the addition of high content of fly ash had an obvious dilution effect, which increased the reaction degree of cement and reduced CO2 emissions for 1 MPa strength. Moreover, the extension of the design age could reduce CO2 emissions for 1 MPa strength. Third, a genetic-algorithm-based optimal design model is proposed to find the individual mass of cement and fly ash of low-CO2 concrete. The analysis results show that, as the water contents increased from 160 to 170 kg/m3, to obtain the same strength, cement mass and fly ash mass increased, while the water/binder ratio and fly ash/binder ratio did not change. This means that the reduction in mixed water is one feasible way to lower CO2 emissions. In summary, the proposed strength–emission integrated analysis method is useful for designing sustainable fly ash composite concrete with the desired strength and low levels of CO2 emissions.

Evaluation of the compressive strength of a reinforced concrete structure using different SonReb estimation methods

This document discusses the results obtained in the estimation of compression strength through the readings of the rebound hammer and ultrasonic pulse velocity on 9 columns of reinforced concrete. The estimation was performed by applying different SonReb models. The objective of the study is to evaluate which of the applied models is more consistent with the compression resistance value of 27.0 MPa obtained by press-breaking assay, for concretes manufactured with Ordinary Portland Cements P-35 (Type I) of domestic production and aggregates from Cuban quarries. The results obtained showed that the models proposed by RILEM and Tanigawa et al. were closest to the reference value with percentage difference below 4%. This research will allow the performance of non-destructive tests with more accurate results, in the restoration processes of the Historic Center of Havana, Cuba.

A Novel Multi-Variable Model for the Estimation of Compressive Strength of Pervious Concrete

A Novel Multi-Variable Model for the Estimation of Compressive Strength of Pervious Concrete

Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model

In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast‐furnace slag (FS) and fly ash (FA) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR − PSO and SVR − GA, that are a hybridization of support vector regression (SVR) with improved particle swarm algorithm (PSO) and genetic algorithm (GA). Furthermore, the hybrid models (i.e., SVR − PSO and SVR − GA) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the hyperparameters of the SVR model, which have a significant influence on model accuracy. The suggested models are evaluated against extreme learning machine (ELM) via quantitative and visual evaluations. The models are evaluated using eight statistical parameters, and then the SVR‐PSO has provided the highest accuracy than comparative models. For instance, the SVR − PSO during the testing phase provided fewer root mean square error (RMSE) with 1.386 MPa, a higher Nash–Sutcliffe model efficiency coefficient (NE) of 0.972, and lower uncertainty at 95% (U95) with 28.776%. On the other hand, the SVR − GA and ELM models provide lower accuracy with RMSE of 2.826 MPa and 2.180, NE with 0.883 and 0.930, and U95with 518.686 183.182, respectively. Sensitivity analysis is carried out to select the influential parameters that significantly affect CS. Overall, the proposed model showed a good prediction of CS of concrete where cement is partially replaced and outperformed 14 models developed in the previous studies.

A numerical integrated approach for the estimation of the uniaxial compression strength of rock from point load tests

The point load test (PLT) has been considered a flexible approach to estimate the uniaxial compressive strength (UCS) of rocks. Previously, empirical equations were obtained by mathematical fitting or machine learning to predict the UCS of rocks. The acquisition of these equations usually required a large amount of experimental data, while the corresponding parameters often lacked clear physical meanings, and the applicability of these empirical equations was limited. In this work, we attempted to develop a new method to predict the UCS of rocks by using the concept of a digital twin (numerical modelling). First, an automatic calibration procedure was used to obtain the numerical parameters of a digital twin for the PLT. Next, the UCS was predicted numerically by using a digital twin of the UCS test with the calibrated parameters. We performed a comprehensive comparison of our proposed method with previously obtained empirical equations and showed the superiority of our approach in better predicting the UCS of rocks. In this paper, we also discuss the influence of particle size and heterogeneity of rock material to illustrate the possible merits of the proposed method. Our work also shows the possible benefits of integrating numerical modelling into physical experimental tests of rocks.

A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures

Fiber-reinforced concrete (FRC) exhibits high fire-resistance capacity and maintains structural integrity at elevated temperatures. However, conventional approaches for optimizing its mixture design and predicting its corresponding mechanical responses following fire exposure present particular difficulties in efficiency, accuracy, and safety issues. To address these issues, a convolution-based deep learning model was developed in the present paper. A dataset with 19 features, including concrete mix proportioning, heating profile, and fiber properties, was collected from previous experimental recordings to evaluate the model performance. The feasibility and generality of the proposed model were validated through the collected dataset and another widely used concrete dataset, where our model performs the best compared with multiple machine learning baseline models. In addition, the correlation between temperature and the relative compressive strength obtained by the proposed model echoes with Eurocode 2, which further demonstrates that our proposed model can accurately estimate the mechanical performances of FRC exposed to high temperatures. It is envisioned that the proposed deep-learning approach serves as an accurate and flexible property assessment tool that aids researchers and engineers in mixture design optimization and compressive strength estimation of FRC for different engineering needs.

Improving uniaxial compressive strength estimation of carbonate sedimentary rocks by combining minimally invasive and non-destructive techniques

Uniaxial compressive strength (UCS) is the most used parameter to measure rock strength. However, restrictions in sampling large volume of material, the need of very large set of results and onsite characterisation of UCS non-destructively are requirements in many scientific and engineering investigations. The estimation of UCS from a single non-destructive or minimally invasive technique (NDT) may result incomplete because each NDT is sensitive to different compositional and textural factors. This paper combines open porosity, P-wave velocity, Leeb hardness and micro-drilling resistance force to estimate USC for a wide range of carbonate sedimentary rock types with different petrographic characteristics. Results reveal that mineralogical composition significatively affects micro-drilling resistance force profiles and P-wave velocity values, especially for quartz-bearing rocks. In addition, texture controls substantially the reproducibility of tests sensible to rock surface properties, such as Leeb hardness and micro-drilling resistance force. Fifteen simple and multiple expressions for UCS are fitted. Linear expressions have shown better coefficients of determination (R 2 ) than non-linear equations because of the linearity shown by individual parameters. Curve fitting improves as the number of petrophysical parameters increase in the multiple linear regression analysis. The best correlation is found when the equation incorporates all the mechanical parameters obtained non-destructively as well as open porosity (R 2 = 0.910). Leeb hardness is always the most significant variable of the fitted regressions and its addition into multiple linear equations causes an increase of R 2 . Open porosity also improves R 2 whereas drilling force and P-wave velocity have a lower statistical weight in the expressions. The UCS estimation from all NDT, without considering open porosity, shows a good correlation (R 2 = 0.899), which presents the advantage that they can be obtained non-destructively with portable equipment and can provide a numerous set of results at relatively low cost. • Compositional and textural factors affect differently to the mechanical parameters. • Leeb hardness is the most significant variable in the fitted regressions. • The best UCS estimation incorporates all NDT parameters as well as open porosity. • Portability is one of the main advantages of NDTs.

Estimation of Unconfined Compressive Strength (UCS) of Carbonate Rocks by Index Mechanical Tests and Specimen Size Properties: Central Alborz Zone of Iran

Estimation of Unconfined Compressive Strength (UCS) of Carbonate Rocks by Index Mechanical Tests and Specimen Size Properties: Central Alborz Zone of Iran