Actual Curve Research Articles

Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures

Many studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random search, fine-tuning method, and the k-fold cross-validation, to derive the seismic fragility curve for accelerating seismic risk assessment. Proposed ML methods significantly reduced the computational efforts compared to conventional procedure of seismic fragility assessment. The prediction results can be combined with considered hazard curves for the purpose of seismic risk assessment of RC buildings. To prepare the training dataset, Incremental Dynamic Analyses (IDAs) were performed on 165 RC frames to achieve 1121184 data points. Performance indicators showed that the algorithms of Artificial Neural Networks (ANNs), Extra-Trees Regressor (ETR), Extremely Randomized Tree Regressor (ERTR), Bagging Regressor (BR), Extreme Gradient Boosting (XGBoost), and Histogram-based Gradient Boosting Regression (HGBR) had higher performance, which achieved acceptable accuracy and fitted to actual curves. In addition, Graphical User Interface (GUI) was introduced as a practical tool yet reliable for seismic risk assessment of RC buildings.

Liquid Loading of Horizontal Gas Wells in Changbei Gas Field

The Changbei gas field, which initially exhibited high gas-production performance, is dominated by large-displacement horizontal wells. With the decrease in reservoir pressure, the liquid loading in the gas well is currently severe, and production has been rapidly decreasing. Thus, recognizing the gas-well liquid loading to maintain stable gas-well production is necessary. A method was established to identify the water source of the liquid loading in the Changbei gas field. First, formation water and condensate water were identified based on the mineralization of the recovered water and the mass concentration of Cl− and K+ + Na+, and then the condensate content of the water produced in the gas well was qualitatively evaluated. The water–gas ratio curve for the gas well was plotted to determine whether the produced water was edge-bottom water, pore water, or condensate. Then a method was established to distinguish the start time of liquid loading in the gas well using a curve depicting a decrease in production; the method was also used to estimate the depth of the gas well where liquid loading occurs, according to the bottomhole pressure. First, based on the available production data, the Arps decline model was applied to fit the production curve for the entire production phase; the resulting curve was compared with the actual production curve of the gas well, and the two curves diverged when fluid accumulation began in the gas well. Finally, the liquid-loading depth of the gas well was estimated based on the bottomhole pressure. This method can be used to determine the fluid accumulation and calculate the liquid-loading depth of gas wells with unconnected oil jackets. The analysis revealed that in the Changbei gas field, condensate was the type of water primarily produced in 35 gas wells, accounting for 62.5% of the total number of gas wells. Edge-bottom water was the type of water primarily produced in 16 gas wells, accounting for 28.6% of the total number of gas wells. In the remainder of the gas wells, pore water was the water primarily produced; the calculations of accumulation time and accumulation volume of typical gas wells in the block revealed that some gas wells started to accumulate liquid after 45–50 months, and the amount of accumulation could reach several tens of meters, while others were in good production condition. The method established in this paper could enhance our understanding of liquid loading in gas wells in the Changbei gas field and lay a foundation for the development of gas-well deliquification techniques.

Study on looseness characteristics of bolted joints of transmission towers under lateral vibration

transmission towers have been subjected to alternating loads caused by the galloping of iced conductors for a long time, and their fastening bolts are easy to loosen or break, which seriously threatens the safe operation of transmission lines. Through the transverse vibration test of M20 bolts commonly used in transmission towers, the vibration frequency and amplitude are changed, and the change law of the preload of bolt joints is studied. The results show that with the increase of the initial preload, the decline speed of bolt clamping force decreases significantly; The clamping force drop curve of M20 bolt is fitted by shivakumar crews model, which is very consistent with the actual test curve.

Predicting the Efficiency of Solar Photovoltaic Energy Injection in a Localized Subtropical Grid by Modelling Actual Generation Trend Curves: Case Study of Douala

Predicting the Efficiency of Solar Photovoltaic Energy Injection in a Localized Subtropical Grid by Modelling Actual Generation Trend Curves: Case Study of Douala

Grid connected power regulation strategy of weak rural energy storage batteries based on particle swarm optimization algorithm

In recent years, the research and application of distributed energy storage technology have received high attention from countries around the world and have achieved rapid development. Integrating distributed energy storage devices into the power grid is one of the effective ways to solve the problem of power quality in weak rural areas. Based on particle swarm optimization algorithm, this paper studies the regulation strategy of integrating distributed energy storage systems into weak rural areas to improve power quality. Based on the actual daily load curve of the distribution station area, optimization regulation strategies are obtained to generate success rate instructions to guide the actual grid connection operation process of the energy storage system.

Proposed stress block for no coarse-aggregate concrete

Concrete is one of the materials in construction that continues to develop in strength and efficiency. Since most areas in Indonesia are vulnerable to earthquakes, the building structures must be more resistant to seismic forces. Increasing the concrete's strength might reduce the building structure's dimensions and weight, which will also reduced the earthquake force. One example of high-strength concrete is reactive powder concrete (RPC), an innovative concrete with small particle materials to fill in the space within the concrete so it can strengthen the concrete. Despite the advantage, there is still a lack of design provisions for this type of concrete. This research was conducted to analyze the stress-strain diagram curve and the compressive stress block for no-coarse aggregtae concrete. In this research, compression tests have been carried out on 100 mm × 200 mm cylinder samples with concrete compressive strength ranges from 28 to 76 N/mm2. Test results indicate that the actual curve of the stress-strain relationship for concrete without coarse aggregate is almost linear and shaped like a triangle with the maximum strain ranges from 0.006 to 0.008. The energy per unit volume ranges from 0.1175 to 0.3658 N/mm2 and the average force capture point is 0.62626 units. Based on the test results, compression stress block for no-coarse aggregtae concrete are proposed.

FRACTIONAL ORDER PIλDμ FOR TRACKING CONTROL OF A NOVEL REHABILITATION ROBOT BASED ON IIMO-BP NEURAL NETWORK ALGORITHM

In this study, we develop a novel multi-posture lower limb rehabilitation robot with three postures, which can provide different amplitudes and frequencies of rehabilitation training for hip, knee and ankle joints, respectively. The kinematic and dynamic analyses of the robot are carried out to solve the kinematic forward and backward solutions and the Lagrangian dynamics equations of the lower limbs. The angle, angular velocity and angular acceleration ideal trajectory curves of the rehabilitation motion are derived by using a quintic polynomial trajectory planning scheme. An improved ions motion optimization (IIMO) algorithm is proposed and applied to optimize the initial weight of back propagation (BP) neural network, and algorithm is used to adjust five parameters of fractional order [Formula: see text] ([Formula: see text]) control in controller design. The passive training experiment results of prototype show that the designed controller has the largest average error of angle and angular velocity of hip, knee and ankle joints in high amplitude and high frequency movement mode, which are 1.091∘, 0.716∘, 0.412∘, 1.551∘/s, 1.394∘/s, 1.498∘/s, respectively. At low amplitude and low frequency, the maximum average errors are the smallest, which are 0.351∘, 0.341∘, 0.167∘; 0.833∘/s, 0.842∘/s, 0.398∘/s, respectively. The actual trajectory curve fits well with the designed one. The highest accuracy of angle and angular velocity can reach 99.165% and 99.116% through comprehensive comparison of all motion modes. Therefore, the overall error is small. The stability of rehabilitation training process is ensured, and the rationality and effectiveness of trajectory planning and control design are verified.

SIMULATION OF DYNAMIC EXPLOSIVE LOADING OF THE BODY OF A SPECIALIZED ARMORED VEHICLE

The aim of the work is a simulation of dynamic explosive loading of the body of a specialized armored vehicle based on the development of theoretical foundations of shape change under the action of pulsed influence and calculation of power parameters. The further equation of the stress state at the point of the material under the conditions of pulse loading was obtained, the methods for determining the principal stresses and the invariant of the stress tensor considering the pulse nature of the load were established. The nature of the formed shock wave behavior due to the detonation of an explosive has been established. Analytical dependencies of the interaction of the shock wave with the loaded surface are made. A mathematical apparatus for calculating such parameters of the shock wave as the pressure of the detonation front and its change in time and the velocity of the shock wave at the time of reaching the surface has been developed. The authors developed and proposed an iterative procedure that allows determining the current values of stresses and strains passing through the points of the actual stresses curve, as well as the stresses and strains intensity during pulse loading of metals. A qualitative analysis of the developed models is performed and the values obtained during numerical simulation in the Ansys AUTODYN of the stress-strain state of workpieces during the explosive expansion using an iterative procedure. Using the analytical method proposed in the work, the theoretical calculation of mine resistance of the MPV's bodies was performed in accordance with the requirements of the NATO AEP-55 STANAG 4569 standardization agreement. The solution to this problem can be used in the future to evaluate the plastic properties of materials during welding and blast hardening; with explosion stamping of blanks and combined welding and explosion stamping operations; with impulse methods of stamping - magnetic-pulse, electro-hydraulic, gas detonation, etc., when parts of the workpiece collide with the matrix, and the other part continues to deform; when stamping on hammers; when crushing materials by the impact of a rigid body; explosion crushing of multicomponent environments; determined ballistic resistance of elements of combat equipment. Keywords: simulation, explosive loading, impulse impact, specialized armored vehicle, mine resistance.

Sequential monoscopic image-guided motion compensation in tomotherapy stereotactic body radiotherapy (SBRT) for prostate cancer.

To manage intra-fractional motions, recent developments in tomotherapy enable a unique capability of adjusting MLC/jaw to track the moving target based on the intra-fractional motions detected by sequential monoscopic imaging. In this study, we evaluated the effectiveness of motion compensation with a realistic imaging rate for prostate stereotactic body radiotherapy (SBRT). The obtained results will guide optimizing treatment parameters and image-guided radiation therapy (IGRT) in tomotherapy using this approach. Ten retrospective prostate cases with actual prostate motion curves previously recorded through the Calypso system were used in this study. Based on the recorded peak-to-peak motion, these cases represented either large(>5mm) or median (≤ 5mm) intra-fractional prostate motions. All the cases were re-planned on tomotherapy using 35Gy/5 fractions SBRT regimen and three different jaw settings of 1cm static, 2.5cm static, and 2.5cm dynamic jaw. Two motion compensation methods were evaluated: a complete compensation that adjusted the jaw and MLC every 0.1 s (the same rate as the Calypso motion trace), and a realistic compensation that adjusted the jaw and MLC at an average imaging interval of 6 s from sequential monoscopic images. An in-house 4D dose calculation software was then applied to calculate the dosimetric outcomes from the original motion-free plan, the motion-contaminated plan, and the two abovementioned motion-compensated plans. During the process, various imaging rates were also simulated in one case with unusually large motions to quantify the impact of the KV-imaging rate on the effectiveness of motion compensation. The effectiveness of motion compensation was evaluated based on the PTV coverage and OAR sparing. Without any motion-compensation, the PTV coverage (PTV V100%) of patients with large prostate motions decreased remarkably to 55%-82% when planning with the 1cm jaw but to a less level of 67-94% with the 2.5cm jaw. In contrast, motion compensation improved the PTV coverage(>92%) when combined with the 2.5cm jaw, but less effective, around 75%-94%, with the 1cm jaw. For OAR sparing, the bladder D1cc, bladder D10cc, and rectum D1cc all increased in the motion-contaminated plans. Motion compensation improved OAR sparing to the equivalent level of the original motion-free plans. For patients with median prostate motion, motion-induced degradation in PTV coverage was only observed when planning with the 1cm jaw. After motion compensation, the PTV coverage improved to better than 94% for all three jaw settings. Additionally, the effectiveness of motion compensation depends on the imaging rate. Motion compensation with a typical rate of two KV images per gantry rotation effectively reduces motion-induced dosimetric uncertainties. However, a higher imaging rate is recommended when planning with a 1cm jaw for patients with large motions. Our results demonstrated that the performance of sequential monoscopic imaging-guided motion compensation on tomotherapy depends on the amplitude of intra-fractional prostate motion, the plan parameter settings, especially jaw setting, gantry rotation, and the imaging rate for motion compensation. Creating a patient-specific imaging guidance protocol is essential to balance the effectiveness of motion compensation and achievable imaging rate for intra-fractional motion tracking.

Short-term Power Load Forecasting Based on Improved BP Neural Network from Genetic Algorithm and Simulated Annealing Algorithm

As one of the common methods of load forecasting, the traditional BP neural network algorithm has been favored by scholars for many years due to its strong learning ability and adaptive ability, and is often used by scholars to forecast power load. However, BP neural network also has its own shortcomings, that is, low algorithm efficiency, local minimization and other problems. In this paper, GA algorithm and SA algorithm are used to modify the weights of BP neural network, and a short-term load forecasting model, namely BP-GSA model, is established. The least mean square error prediction model is obtained through training. Then using BP-GSA model and traditional BP neural network model, using MATLAB software to predict the daily power load of a city from July 1 to July 10. The forecast results show that the load forecast curve obtained by BP-GSA model is closer to the actual load curve. That is to say, BP-GSA load forecasting error is less than the traditional BP neural network load forecasting error, which proves that BP-GSA model has better forecasting effect.

Simulation of wall materials with isotope concentration gradient for corrosion degree calibration in ITER

In the ITER reactor, the degree of corrosion of the wall is monitored by detecting the concentration of the isotope injected into the wall to ensure safe operation. Therefore, a wall material with an isotope concentration gradient that can be easily monitored must be developed. In this study, we adopted TRIM, Monte Carlo (M-C), and N (X) to predict the concentration distribution of isotopes injected into wall materials. The concentration peak and depth range of the isotope concentration distribution curve calculated by the TRIM program were very different, and the deviation was as high as 2.70%. Combined with the Monte Carlo (M-C) calculation method and the modified longitudinal static stability theory (LSS), the simulated isotope concentration distribution curve was in good agreement with the actual detection curve. However, the result was discontinuous, so the deviation could not be calculated. The N (X) simulation calculation exhibited a high degree of agreement, and the deviation was only 0.67%, so it may be considered suitable for the simulation of the concentration distribution of ion implantation in wall materials under various conditions.

Forecasting delay-time model for Saudi construction projects using DEMATEL–SD technique

Construction delays are common in the construction industry in Saudi Arabia. This study aims to develop a simulation forecasting model for the delay time in Saudi projects by considering ten previously identified factors. This study employs a systematic, integrated approach of the decision-making trial and evaluation laboratory (DEMATEL) methodology and system dynamics (SD) to reflect the ten factors that impact project delay. The DEMATEL technique is used in this study to solve the difficulty of systematically developing a causal loop diagram within the SD modelling process. The developed SD model is tested for consistency and extreme conditions. It is then validated and applied using three case studies (an academic building, administrative building, and infrastructure project) in Saudi Arabia by comparing the actual and fitted progress curves of each case study. The results show that the proposed model can be a respectable aid to project managers in forecasting Saudi projects wherein the actual monthly progress follows an S-curve. In this regard, three satisfaction conditions are introduced to check the project progress in the S-curves. In addition, this study provides a systematic approach combining the DEMATEL and SD techniques, which can simplify the SD modelling process in the future. Furthermore, the study methodology can be repeated in other countries.

Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters

ObjectivesTo investigate the prognostic value of [18F]FDG PET/CT parameters in local recurrent nasopharyngeal carcinoma (lrNPC) and establish a prognostic tool for lrNPC patients based on these [18F]FDG PET/CT parameters.MethodsA total of 358 lrNPC patients seen from 2010 to 2019 at Sun Yat-sen University Cancer Center with complete baseline characteristics and [18F]FDG PET/CT data were retrospectively analyzed. Maximal standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) for recurrent nasopharynx tumors were included. Cox regression analysis was performed to select candidate variables. Subsequently, a nomogram for predicting overall survival (OS) for lrNPC patients was developed and internally validated.ResultsMultivariate Cox analysis results suggested that age ≥ 47 years (hazard ratio (HR), 1.62 (1.18-2.24); p = 0.003)，with smoking history (HR, 1.41 (1.01–1.98); p = 0.046), recurrent T stage {[rT3 vs rT1/2: HR, 1.81 (1.04–3.12); p = 0.037]; [rT4 vs rT1/2: HR, 2.46 (1.32–4.60); p = 0.005]}, and TLG {[37.1–184.3 vs ≤ 37.1: HR, 2.26 (1.49–3.42); p < 0.001]; [>184.3 vs ≤ 37.1: HR, 4.31 (2.50–7.43); p < 0.001]) were independent predictors of OS. A 4-factor nomogram was generated to stratify patients into 3 risk groups. This novel model showed good discrimination with a high C-index (0.752, 95%CI: 0.714–0.790). In addition, the calibration curves showed good agreement between the predicted probabilities and actual observations and decision curve analysis (DCA) suggested that the nomogram was useful for clinical decision-making.ConclusionsOur study confirmed that [18F]FDG PET/CT parameters were valuable in predicting OS and PFS for lrNPC patients. The 4-factor prognostic model combing baseline patient characteristics with [18F]FDG PET/CT parameters for lrNPC patients had good discrimination, agreement, and clinical application potential.Key Points•[18F]FDGPET/CTparameters were valuable in predicting OS and PFS for lrNPC patients.• The novel 4-factor nomogram for lrNPC patients had good discrimination, agreement, and potential for clinical application.

Determination of the size of rock fragments using RVM, GPR, and MPMR

For predicting the size of rock fragments during drilling and blasting operations, this article uses GPR, RVM, and MPMR. The current analysis makes use of a blast data set generated in a prior investigation. In this study, a portion of the blast data was utilized to train a model to determine the mean particle size arising from blast fragmentation for each of the similarity groups generated. The particle size was modeled as a function of seven different variables. The dataset contains information about the bench height and drilled burden ratio (H / B), spacing to burden ratio (S / B), burden to hole diameter ratio (B / D), stemming to burden ratio (T / B), powder factor (Pf ), modulus of elasticity (E), and in-situ block size (XB) are the input and output is X50. By comparing forecasts with actual mean particle size values and predictions based on one of the most widely used fragmentation estimation techniques in the blasted literature, the capacity of the generated models may be established. The statistical parameters, actual vs predicted curve, Taylor diagram, error bar, and developed discrepancy ratio are used to analysis the performance of models. A comparative study has been carried out between the developed RVM, GPR, and MPMR. The results show the developed models have the capability for prediction of X50. From these comparisons, the MPMR has the highest value with a high degree of precision and robustness in the size of rock fragments X50.

Real-Time Experimental Monitoring for Water Absorption Evolution Behaviors of Sandstone in Mogao Grottoes, China

Rock mass has typical pore structure, and the induced coupling effects of fluid and solid matrix appear in the disaster evolution process of deep energy exploitation and overground rock hydration. As a representative case, influenced by the water absorption environment, the surrounding rock and murals of Mogao Grottoes produce hydration diseases, which may be related to unclear interaction mechanisms between the surrounding rock and water. In this study, the self-developed physical experimental system for real-time experimental monitoring was applied to test the water absorption evolution behaviors of sandstone. The experimental results showed that the water evaporation of the rock sample during the process of water absorption could be measured through this well-designed physical experimental system, and the actual water absorption of the rock sample is the difference between the decrease of water in the water storage bucket, measured by the balance and the water evaporation in the process of experiment; by drawing the actual water absorption curve of the rock sample, the time when the water absorption of the rock sample reaches saturation could be determined accurately; and the curve of water absorption with time could be expressed as an exponential function. The experimental techniques and methods in this study provide a feasible research idea for studying the water absorption evolution behaviors and mechanisms of the surrounding rock weathering when it meets water, and have significance for revealing the disease mechanisms of the surrounding sandstone in Mogao Grottoes, China.

Numerical Evaluation of the Fracture Process of 41Cr4 Steel: Analysis of Cracks Grown in a Plane Strain State Domination Based on Experimental Results.

The paper presents the evaluation of the fracture process of 41Cr4 steel, based on experimental tests and numerical analysis carried out for growing cracks. Selected results of experimental studies presented in previous publications in 2015–2016 which were used in the construction of FEM models of SEN(B) specimens dominated by a plane strain state, are quoted. Using FEM simulations, the tensile curves and crack growth curves recorded during laboratory tests, several calculations were performed, among which can be distinguished the evaluation of plastic zones in the considered specimens, stress distributions, selected measures of geometric constraints, accumulated plastic strains and selected parameters of fracture mechanics. The numerical calculations presented in this paper allow to estimate the changes of the J-integral for the tested steel, the crack tip opening displacement, the size of the plastic zone and the stress distribution in front of the crack tip. Comprehensive numerical analysis with the use of the actual tensile curve clearly characterizes the behavior of the material under the influence of increasing external loads.

Development and validation of a prognostic nomogram for decompensated liver cirrhosis.

BACKGROUNDDecompensated liver cirrhosis (DLC) is a stage in the progression of liver cirrhosis and has a high mortality. AIMTo establish and validate a novel and simple-to-use predictive nomogram for evaluating the prognosis of DLC patients.METHODSA total of 493 patients with confirmed DLC were enrolled from The First Affiliated Hospital of Nanchang University (Nanchang, Jiangxi Province, China) between December 2013 and August 2019. The patients were divided into two groups: a derivation group (n = 329) and a validation group (n = 164). Univariate and multivariate Cox regression analyses were performed to assess prognostic factors. The performance of the nomogram was determined by its calibration, discrimination, and clinical usefulness.RESULTSAge, mechanical ventilation application, model for end-stage liver disease (MELD) score, mean arterial blood pressure, and arterial oxygen partial pressure/inhaled oxygen concentration were used to construct the model. The C-indexes of the nomogram in the derivation and validation groups were 0.780 (95%CI: 0.670-0.889) and 0.792 (95%CI: 0.698-0.886), respectively. The calibration curve exhibited good consistency with the actual observation curve in both sets. In addition, decision curve analysis indicated that our nomogram was useful in clinical practice.CONCLUSIONA simple-to-use novel nomogram based on a large Asian cohort was established and validated and exhibited improved performance compared with the Child-Turcotte-Pugh and MELD scores. For patients with DLC, the proposed nomogram may be helpful in guiding clinicians in treatment allocation and may assist in prognosis prediction.

Data-driven reduced order model and simplicial homology global optimization for reliability analysis and application

A novel framework of reliability analysis was developed in this study to consider the uncertainty of geomaterials and geological conditions by combining the reduced-order model (ROM), reliability analysis, and numerical model. The reliability method was used to determine the reliability index using the simplicial homology global optimization (SHGO) based on the ROM. The developed method was verified and illustrated using three numerical examples and a simple slope. The limit state curve in all three numerical examples was in excellent agreement with the actual curve. The reliability index and failure probability were also in excellent agreement with those of the actual limit state function using the first-order reliability method (FORM) and Monte Carlo simulation, respectively, indicating that the ROM method can present the limit state function well. The results showed that the developed method is feasible and effective for reliability analysis of geotechnical and geological engineering problems with a complex, nonlinear, and implicit limit state function. Furthermore, the developed method is effective, efficient, and accurate for reliability analysis. It provides an excellent way to approximate the limit state function to avoid the time-consuming numerical model in a practical engineering system.

Determination of Heat Transfer Coefficient by Inverse Analyzing for Selective Laser Melting (SLM) of AlSi10Mg

Heat treatment can improve performance and control quality in the additive manufacturing process. In the numerical simulation of heat treatment, the accuracy of the heat transfer coefficient will have a significant impact on the accuracy of the simulated temperature field. At present, The inverse analysis method is the most common and effective method to determine the heat transfer coefficient. Taking the actual temperature curve as the input condition, the heat transfer coefficient values of the heating, quenching, and air cooling components in the heat treatment process are successfully obtained. Based on the obtained heat transfer coefficient, a mathematical model of the heat transfer coefficient change with temperature during heat treatment is established. The heat transfer coefficient obtained by the inverse analysis method is then applied to the simulation of heat treatment, and more accurate simulation results are obtained. It is proven in this work that the inverse analysis method can improve the accuracy of the simulation model in the heat treatment process of AlSi10Mg.

Analyzing the Effectiveness of COVID-19 Mitigation Policies Using ARIMA Forecasting

The SARS-CoV-2 virus has triggered a worldwide pandemic situation which countries are desperately trying to adapt to. In order to halt the transmission of this virus, these countries have implemented COVID-19 mitigation policies, which are designed to suppress the spread and deadliness of the virus. However, there has not been much research into the effectiveness of these COVID-19 mitigation policies. Using data from the Kaggle Platform as well as the European Centre for Disease Prevention and Control, we hope to use an ARIMA time series forecasting model in order to identify effective COVID-19 mitigation polices. This will be done by analyzing the cases time series before the mitigation policy was implemented in a certain country and generating a predicted forecast curve during the time range of the mitigation policy. By comparing this generated curve with the actual curve, deviations will be able to be identified, indicating the significance of the mitigation policy during its implementation. Although most forecasting was relatively inaccurate due to a shortage in training data, one social distancing mitigation policy in South Korea had a clear deviation between the forecast curve without the influence of the mitigation policy and the actual curve. Overall, the ARIMA model has its merits and may prove to be useful with the collection of more data. By analyzing the effectiveness of these policies, future research into this topic may lead to a greater understanding about the transmission of COVID-19 and ways to suppress it.