Error Method Research Articles

Research on calibration method for measurement error of multiple rate carrier energy meter

Abstract With the rapid development of smart grid technology, the multi-rate carrier watt-hour meter is an important measurement tool in power systems; its accuracy directly affects economic benefits and energy management efficiency. This paper proposes a calibration method of measurement error for a multi-rate carrier watt-hour meter to improve the measurement accuracy and reliability of the watt-hour meter under changing load conditions. The multi-rate carrier energy data is collected in real-time by high-precision data acquisition equipment to ensure the comprehensiveness and accuracy of the data. Using advanced data analysis technology to extract the amplitude characteristics of abnormal metering data, on this basis, the output deviation of equipment without input signal is eliminated, and the metering error calibration of the electric energy meter is completed. The experimental results show that this method can significantly improve the metering accuracy of multi-rate carrier watt-hour meters under different power consumption conditions and has good adaptability and practical value.

Biodegradable Schiff bases: a novel approach for the management of pathogenic fungi (Sclerotium rolfsii and Rhizoctonia bataticola) and stored grain insect (Callosobruchus maculatus) in green gram (Vigna radiata).

Twenty-two eco-friendly, novel Schiff bases were synthesized from 2,4,5-trichloro aniline and characterized by using FT-IR, 1H NMR, and 13C NMR techniques. Fungicidal activity against pathogenic fungi Sclerotium rolfsii and Rhizoctonia bataticola and insecticidal activity against the stored grain insect pest Callosobruchus maculatus of the test compounds were evaluated under control condition. All of the investigated compounds, according to the study, exhibited moderate to good antifungal and insecticidal activities. The best antifungal activity against both pathogenic fungi was demonstrated by C15 and C16 whose ED50 values were recorded 11.4 and 10.4μg/mL against R. bataticola and 10.6 and 11.9μg/mL against S. rolfsii, respectively. They were further screened in for disease suppression against both pathogenic fungi under pot condition through different methods of applications in green gram (Vigna radiata L.) crop. The compounds C10 and C18 had the highest insecticidal activity, with LD50 values of 0.024 and 0.042 percentages, respectively. Stepwise regression analysis using root mean square error (RMSE) and correlation coefficient (R) method used to validate the quantitative structure activity relationship (QSAR) of synthesized compounds in addition to their fungicidal and insecticidal actions. To the best of our knowledge, this investigation on the 22 new Schiff bases as possible agrochemicals is the first one that has been fully reported.

A liter scale synthesis of hierarchically mesoporous UiO-66 for removal of large antibiotics from wastewater.

The presence of antibiotics in water sources is a significant concern due to their potential environmental impact and the risks to human health. In the present research, hierarchically mesoporous UiO-66 (HP-UiO-66) with a high surface area (1011 m2/g) and large pore volume was synthesized using the reflux method on the liter scale. The successful synthesis was confirmed by FT-IR, XRD, FESEM/EDS, N2-adsorption/desorption, and zeta potential techniques. The HP-UiO-66 was utilized to remove two large structure antibiotics, chlortetracycline hydrochloride (CTC), and oxytetracycline (OTC). Box Behnken design was used to investigate the factors affecting the removal process and the interactions between them. The maximum adsorption capacities for OTC and CTC antibiotics were 252.9mg/g and 234.2mg/g at 35°C, respectively. The sum of the normalized error method was applied to the analysis of various error functions in the nonlinear fitting of equilibrium and kinetic data. The CTC and OTC adsorption kinetic followed a fractal-like pseudo-second-order model. The Langmuir isotherm fitted well to adsorption data. The results demonstrate that HP-UiO-66 can be used as a recyclable and efficient adsorbent for large molecule antibiotics removal.

Analysis of Influence of Wiring Error on Electric Energy Metering Device and Preventive Measures

Electric energy metering device is the key to ensure the economic interests of power supply enterprises and users and the safety of electricity. Wiring errors are the main causes of electric energy metering distortion and power line failure. Five types of wiring errors of electric energy metering devices and their possible effects are described and sorted out, and the corresponding detection methods for software and hardware wiring errors are introduced. Based on the actual application of the electric energy metering device, the corresponding preventive measures are put forward, in order to provide some reference for solving the problem of wiring errors in the metering system of the electric energy metering device and ensuring the safety of electricity use.

APPLICATION OF DIFFERENTIAL EQUATIONS IN POPULATION GROWTH ESTIMATION OF KEDIRI CITY AS THE IMPLEMENTATION OF THE 2030 SDGS TARGET

This study focuses on analyzing the application of differential equations in modeling the population growth of Kediri City through a logistic growth model, and aims to predict the city's population for the years 2021-2030. Employing a literature study approach, this research utilizes secondary data sourced from the official website of the BPS (Central Statistics Agency) of Kediri City. The data encompasses population figures from 2005 to 2020. To ensure the accuracy of the population predictions made by the logistic growth model, a comparison with the official data from the Central Statistics Agency is necessary. A critical aspect of this research involves estimating the carrying capacity of Kediri City's population, which is identified as 295,672 individuals in the year 2030. The findings indicate a significant growth trend in the population, characterized by a specific growth rate. To validate the accuracy of the logistic growth model, the Mean Absolute Percentage Error (MAPE) method was applied, resulting in an error percentage that falls within the highly accurate category, thus affirming the reliability of the model in predicting population growth trends.

A multi-agent deep reinforcement learning paradigm to improve the robustness and resilience of grid connected electric vehicle charging stations against the destructive effects of cyber-attacks

The rising deployment of electric vehicle charging stations (EVCS) in existing power grids and their integration with electric vehicles through communication systems are increasingly vulnerable to cyber-attacks, such as false data injection (FDI) and uncertainties of communication system. This research introduces an EVCS evaluation from a techno-economic standpoint, utilizing the developed and data-oriented TSKFS&MADRL technique to identify and rectify inaccuracies stemming from cyber-attacks like FDI and communication system delays. This method aims to enhance the resilience of EVCSs against sabotage attacks by ensuring fast dynamic responses. Initially, the study models the potential targets of hackers, such as communication systems and transducer sensors, and employs the Euclidean norm theory, weighted least square error method, and residual error technique to identify cyber-attacks resulting from FDI through the comparison of measured data with reference values based on probability distribution functions. Subsequently, the network control and recovery requirements are facilitated by the proposed controller. The proposed approach application has been demonstrated in the IEEE 33 bus, showcasing a 7.33 % lower experimental operation cost compared to the RL method and 12.15 % lower than the CNN method. Additionally, the FDI detection time is observed to be 40 % quicker than alternative methods based on the experimental outcomes.

Compensation method of the spiral bevel gear tooth surface error based on eight tooth-surface fitting

Abstract The misalignment error between the gear theoretical coordinate system and actual gear coordinate system is illustrated, as well as its influence on the measurement of tooth surface deviation is discussed. On the analysis of the tooth convex and concave surface error measurement process, the measured points in the theoretical coordinate system are transformed from the actual measured points in the actual coordinate system. Then, an eight tooth-surface fitting method is utilized to calculate the misalignment error and the compensation method is given. Finally, actual measurements of three different spiral bevel gears are analyzed, and the misalignment error are calculated. Using the proposed compensation method, both the eccentricity and tilt are compensated to some extent. And the machining suggestions of each spiral bevel gear are also given according to its compensated tooth surface error distribution.

Joint-Life Endowment Insurance Premium Calculation Based on Makeham Mortality Law

<p>This research determined life insurance premiums joint-life endowment insurance, that is the insurance that the benefit is paid if one of the insureds dies or all the insureds survive until the end of the contract. This premium calculation uses one of the famous mortality laws, that is Makeham. Makeham mortality law is an updated law of Gompertz's mortality law where the reason that makes Makeham different from Gompertz is the death calculation factor, because Gompertz only takes the cause of death from the age factor. Even though the death factor can also come from accidents, therefore, Makeham combined the two causes of death so that it is corresponded to the actual factors. The Makeham law will function to create the Makeham assumption mortality table with trials and errors method to construct Indonesia Mortality Table 2019 (TMI IV). This construction requires parameters that must be founded to determine the survival probability ( ) of the Makeham. After finding the , calculate the annual premium by dividing the net single premium by the life annuity. The joint-life endowment insurance premium calculation in this study uses 10-year protection with a 5.75% for interest rate and Rp1,000,000,000 for the benefit, and based on the research, the annual premium for this married couple is Rp78,016,785, where the premium calculated based on Makeham will get a higher premium than calculated based on TMI 2019, which gets an annual premium Rp77,410,954.</p>

Method of Equivalent Error as a Criterion of the Assessment of the Algorithms Used for Estimation of Synchrophasor Parameters Taken from the Power System.

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error's value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement).

LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.

Prediction of building settlement amount based on the ARIMA-LSTM-GXBoost combined model

Accurate prediction of building settlement amounts is crucial for ensuring the safety of building structures and human lives. Addressing issues such as the scarcity of building settlement measurement data and the lack of weighting in combined prediction model results, which have not yet led to further improvements in prediction accuracy, this study first selected building CJ06 in Yizheng City, Yangzhou, as the research subject. Polynomial fitting was applied to the obtained non-equidistant time series data, and the fitting data with a preferable polynomial order was chosen to replace the original numerical data. Then, using the first 140 days of data as the training set and the subsequent 63 days of data as the test set, an ARIMA (AutoRegressive Integrated Moving Average) model was initially established for the building settlement, to address the linear features in the fitted sequence. This was followed by using LSTM (Long Short-Term Memory) to correct the ARIMA model's prediction residuals, and combining the results with the XGBoost (Extreme Gradient Boosting) prediction model. Weights were determined using the reciprocal error method for combined prediction, and the results of the combined prediction model were compared with those of the ARIMA-LSTM, ARIMA, LSTM, and GXBoost models. The comparison periods were chosen as follows: 143d, 158d, 173d, 188d, and 203d; ultimately, the maximum absolute relative error and mean square error (MSE) were used as the evaluation metrics for model prediction accuracy. The results showed that the ARIMA-LSTM-GXBoost prediction model's results were more closely aligned with actual values, with a maximum absolute relative error and mean square error of 1.68% and 0.03, respectively. The prediction accuracy was higher than the other four comparison models, achieving high-precision prediction of building settlement amounts.

Merging Data with Modeling: An Example from Fatigue.

It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 107 cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted.

Is inflation targeting effective? Lessons from global financial crisis and COVID‐19 pandemic

AbstractThis paper explores the influence of inflation targeting (IT) policy on macroeconomic performance in 24 Asian economies by taking a wide range of macroeconomic variables from 2001 to 2022. Specifically, the study seeks to evaluate the influence of IT on macroeconomic performance during the global financial crisis (GFC) and the COVID‐19 pandemic periods separately. The empirical analysis comprises three approaches: propensity score matching, difference‐in‐differences, and panel‐corrected standard error methods. The empirical investigation reveals that IT significantly impacts inflation and its volatility during the GFC. In contrast, the effect on the unemployment rate is not statistically significant. Further, IT statistically influences the inflation and unemployment rates throughout the COVID‐19 pandemic. In contrast, its impact on inflation volatility during the COVID‐19 pandemic is not evident. Our results have significant policy implications for the Asian economies. It may be suggested that low‐income countries could benefit from implementing IT policy as a tool to ensure stable inflation. However, there is a need for continuous policy adaptation and targeted interventions to address evolving economic challenges, especially in situations like crises.

Strategija za ocjenjivanje učinka formaldehida u anatomskom patološkom laboratoriju treći dio

To date, formaldehyde (FA) is one of the more common chemicals and its use is widespread around the world in different sectors, especially in chemical facilities and health care. FA is widely used in working activities owing to its chemical and physical properties. However, it also represents a concerning hazard for the workers’ health due to its toxicity and recognized carcinogenicity. The FA exposure evaluation in occupational setting has arisen interest in the scientific community that leads to the development of several analytical instruments in order to assess both long term and short-term exposure. The paper presents and discusses an equivalence tests procedure via the 2.4-dinitrophenylhydrazine (DNPH)-active air sampling formaldehyde (FA) reference method and two non-reference instruments based on continuous, direct reading monitoring, namely ProCeas® (AP2E) and NEMo XT (Ethera). The FA standard atmosphere calibration system was used to check the reference method by Pearson’s test. Subsequently, the Passing-Bablok test was carried out between the non-reference methods and the DNPH method for potential systematic or proportional errors, and finally the Bland-Altman plot was applied to determine the mean bias and the variances of the recorded values by the reference and non-reference methods in on-field sampling. The results showed a good correlation between the non-references method and the DNPH ones, suggesting their possible applications in heterogeneous occupational scenarios.

Identification and Compensation Method of Unbalanced Error in Driving Chain for Rate-Integrating Hemispherical Resonator Gyro.

The accuracy of the signal within a driving chain for the rate-integrating hemispherical resonator gyro (RI-HRG) plays a crucial role in the overall performance of the gyro. In this paper, a notable and effective method is proposed to realize the identification and compensation of the unbalanced error in the driving chain for the RI-HRG that improved the performance of the multi-loop control applied in the RI-HRG. Firstly, the assembly inclination and eccentricity error of the hemispherical resonator, the inconsistent metal conductive film layer resistance error of the resonator, the coupling error of the driving chain, and the parameter inconsistency error of the circuit components were considered, and the impact of these errors on the multi-loop control applied in the RI-HRG were analyzed. On this basis, the impact was further summarized as the unbalanced error in the driving chain, which included the unbalanced gain error, equivalent misalignment angle, and unbalanced equivalent misalignment angle error. Then, a model between the unbalanced error in the driving chain and a non-ideal precession angular rate was established, which was applicable to both single channel asynchronous control and dual channel synchronous control of the RI-HRG. Further, an unbalanced error identification and compensation method is proposed by utilizing the RI-HRG output with the virtual precession control. Finally, the effectiveness of the proposed method was verified through simulation and experiments in kind. After error compensation, the zero-bias instability of the RI-HRG was improved from 3.0950°/h to 0.0511°/h. The results of experiments in kind demonstrated that the proposed method can effectively suppress the non-ideal angular rate output caused by the unbalanced error in the driving chain for the RI-HRG, thereby further improving the overall performance of the RI-HRG.

Experimental vibration-based output-only damage localization of mechanical systems

Vibration-based Structural Health Monitoring (SHM) is becoming increasingly widespread in a variety of engineering sectors as its variability enables diverse applications. It enables full-time assessment of the status of a structure, allowing for early maintenance and repair to prevent further damage or even failure. The presented study is part of the SPP100+ program, which has the objective of extending the usability of complex structures. This study presents an experimental investigation of the damage localization of mechanical structures while considering varying environmental and operational conditions (EOC). For this, a testing field was designed and applied, enabling one to study the effects of the varying conditions in a comparable way. The research is based on the approach to modify the state projection estimation error (SP2E) method to account for the influence of EOC. The proposed method utilizes output-only system identification and state space parameterization to estimate the current structural state. Stochastic subspace identification (SSI) and H_∞ -estimation are employed to identify the state space model during a learning phase. Subsequently, the SP2E method is applied during the monitoring phase to localize structural alterations. Moreover, an algorithm is introduced to estimate the weighting factors of different environmental and operational conditions. To verify the efficacy of the proposed method, an extensive experimental study is conducted. The numerical analysis successfully demonstrates the applicability of the method for localizing mass perturbation and stiffness degradation under various EOC. This work contributes to proactive maintenance and damage detection for mechanical structures under varying conditions.

Penentuan Formulasi Terbaik Pembuatan Sampo Kendaraan Berbahan Dasar Sisa Produk Sampo di Industri Sampo

This research aims to process remaining shampoo products originating from the sampo industry into vehicle shampoo so that it has sales value and also has good quality. Determining the formulation for making this vehicle shampoo uses the trial and error method. After that, the variation data obtained will be tested through a pH test, density test, viscosity test, foam power test and corrosivity test. The results obtained are that the remaining variation data for the 25 mL shampoo product is the most optimal data because it meets the requirements for pH, density, viscosity and foaming test results. The results obtained from the variation data to make the best formulation were 47% remaining shampoo, 8% LAS, 8% NaCl (0.5 N), 8% NaOH (0.1 N), 0.4% Essense (Bubble Gum). %, and Aquadest 30%. This formulation produces a pH value of 7.18. Meanwhile, the density is 1.0257 g/cm3. The viscosity of this composition is 2.999 Pa.s. The stability of the foam power is 66,7% and the corrosion resistance test results get a value of 0.145655 mm/year or in the "Good" category in terms of corrosion resistance.

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose.

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10mg/L), contact time (t, 10-1440min), initial pH (pHi, 2-10), temperature (T, 10-55°C), and initial concentration of nitrate (C0, 10-500mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

Improved Parker–Oldenburg method and its application to Moho topographic inversion in the northern South China Sea

SUMMARY Before inverting Moho topography, the traditional Parker–Oldenburg method requires the determination of two important hyperparameters, the average Moho depth and Moho density contrast. The selection of these two hyperparameters will directly affect the inversion results. In this paper, a new method for estimating hyperparameters is proposed which is used to improve the Parker–Oldenburg method. The new method is improved by using simulated annealing to accurately estimate the average Moho depth and Moho density contrast based on the relationship between Moho depths and corresponding gravity anomalies at seismic control points. Synthetic tests show that compared to the improved Bott's method and the trial and error method, our method reduces the error in Moho density contrast and average Moho depth by 0.83 and 1.81 per cent, respectively. In addition, compared with the trial and error method, our method greatly improves the computational efficiency. In a practical example, we apply this method to invert the Moho topography in the northern South China Sea. The inversion results show that the Moho topography in the northern South China Sea ranges from 8.2 to 33 km. The root mean squared error between our Moho topography and the seismic validation points is 0.94 km. Compared with the CRUST 1.0 model, our Moho topography is more accurate.

Stabilitas Paving Block Menggunakan Plastik PET (Polyethylene Terephthalate) Dengan Tambahan Serat Bendrat

Plastic waste is one of the most complex problems especially in the Parepare region. Recycling of plastic waste is rare, so the number of garbage piles increases annually. Paving block made from PET plastic waste is one form of effort to reduce the accumulation of plastic waste. The non biodegradable properties of polyethylene cause the plastic aggregate in paving block not to be easily crushed, so it can keep paving block density over a long period of time. The purpose of this study was to determine the stability and compressive strength of paving blocks made of plastic with a variation of 50% PET, 75% PET and 100% PET with bendrat wire fiber, and to find out what the proportion of plastic and sand is in the paving block mixture, which results in optimal compressive strength. The method used is the experimental trial and error method, by experimenting directly in the laboratory. The experiment in question is to make comparisons of the variables studied, so as to produce conclusions that are correlated with the variables studied. From several variations of the planned pet percentage, only 1 variation has stability, namely the 50% PET variant, and the most optimal proportion of plastic and sand is found at 50% PET. The compressive strength obtained is 14.72 MPa for the cube test object and for the paving block specimen it has a compressive strength of 10.16 MPa.