The Influence of Mesoscopic Characteristics of Mineral Powders Fillers on the Rutting Factor(G^(*)/sinδ)of Asphalt Mortar

This study aims to investigate the efiect of the mesoscopic characteristics of mineral powder fillers on the rutting resistance of asphalt mortar.Extraction and sieving tests were used to obtain the buton rock asphalt(BRA)ash with particle size smaller than 0.075 mm,which is consistent with that of the conventional mineral powder.The mesoscopic characteristics of BRA ash and conventional mineral powder were measured by SEM image analysis and the osmotic free pressure water method.Mesoscopic structure models of structural and free asphalts in mortar were obtained.The 70#matrix asphalt was used to prepare two kinds of asphalt mortar with BRA ash and conventional mineral powders fillers.The rutting factor of the two asphalt mortars was tested by dynamic shear test(DSR).Test results show that the ash extracted from BRA has a similar mesoscopic classification with the conventional mineral powder.Still,its fractal dimensions are larger,indicating the particles in BRA ash have more complex shapes and rougher surfaces,which is beneficial for forming structural asphalt and subsequently increasing the rutting factor(G*/sinδ),i e,improving the rutting resistance of the asphalt mortar.

A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

Analytical Study on the Effect of Electric Arc Furnace Dust(EAFD)on the Rutting of Asphalt Concrete Mixtures

As a byproduct of the steelmaking process,significant amounts of hazardous electric arc furnace dust(EAFD)are produced.Utilizing the solidification/stabilization technology with asphalt mix is one way to safeguard the environment from its negative effects.Rutting was used as an indicator to assess the asphalt mixture with EAFD since it is an important factor in pavement design.This study’s major goal is to ascertain how EAFD affects the rutting of asphalt-concrete mixtures.To evaluate the ideal asphalt content,the Marshall test method was applied to asphalt-concrete mixtures.EAFD was added to the asphalt cement in four different volume percentages as a binder addition.Then,using the Universal Testing Machine,participants were exposed to a replica of the rutting test(UTM).Experiments were conducted at 25℃,40℃ and 55℃,and at frequencies of 1 Hz,4 Hz and 8 Hz.Rutting was measured for each specimen.Test results showed that rut depth has a negative correlation with EAFD%and a positive correlation with temperature.The use of EAFD has dual advantages,protecting the environment from the adverse impact of EAFD and reducing the cost of asphalt mix without jeopardizing pavement performance.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

微织构铣刀干铣削RuT500切削性能研究

为探究微织构对刀具加工蠕墨铸铁RuT500切削性能的影响,改善RuT500的可加工性,利用激光技术在刀具前刀面制备了3种形式的微织构并通过单因素试验方法,在不同切削速度下将微织构刀具和无织构刀具对RuT500进行铣削加工,对比分析了微织构的方向和类型在加工中切削性能的差异。结果表明,表面微织构可以有效降低切削力,改善切屑形态和工件表面质量,在一定程度上降低了前刀面磨损;3种微织构在部分相同切削速度下对切削性能的影响有较大差别;切削速度较大时,微织构更有利于RuT500的切削加工。在3种微织构中,以平行于主切削刃的沟槽织构刀具切削性能最优。

Genetic Optimization Method of Asphalt Pavement Based on Rutting and Cracking Control

Nowadays asphalt pavement structure bearing is not the main subject for pursuers to study.Comparatively,the pavement performance is more important and emphasized.Based on this,rutting and cracking analysis is introduced into pavement optimization.A optimization model based on these two considerations is also established.The genetic algorithms (GAs) is adopted to solve the model.It is an intellective method.This research provides a new idea and technique for asphalt pavement structure optimization.

Rutting Resistance of HMA Rehabilitated with Micro-Surfacing

The work presented here is a study on the measurement and prediction of the rutting resistance of previously rutted asphalt mixes rehabilitated with a layer of micro-surfacing manufactured with virgin and recycled aggregates at different stages of aging. The experimental procedure consisted of rutting tests on hot mix asphalt slabs already degraded and repaired with virgin and recycled micro-surfacing. Then, the evolution of the behavior of micro-surfacing cast on the hot mix asphalt slabs is observed according to loading cycles of the pavement rutting tester MLPC. Before rutting tests, slabs are subjected to 24 hours at 50&degC and aged for 2 days and 5 days at 85&degC in the oven. The results showed rutting percentages of 6.3% for hot mix asphalt slabs aged for 2 days and 7.2% for 5 days. These hot mix slabs repaired with virgin micro-surfacing have rutting percentage of about 9.2 % for 2 days of aging and 6.5% for 5 days of aging. While, the HMA slabs repaired with recycled micro-surfacing have rutting percentage of about 8.1% for 2 days of aging and 5.9% for 5 days of aging. These results allowed the development of a prediction model based essentially on three predictor variables including cycle number, rutting state and percentage of water in the micro-surfacing material. The developed model shows a strong correlation between the predicted rutting values and the rutting values measured with the MLPC rut tester. Thermal aging in oven has a positive impact on the resistance to permanent deformation of new asphalt mixes and those rehabilitated with micro-surfacing. The parameters of rutting state and contribution water are significant in the rutting prediction model, while the cycle number remains a non-significant parameter in the model but determinant.

Experimental Characterization of Rutting Performance of HMA Designed with Aggregate Gradations According to Superpave and Marshall Methods

The permanent deformation (rutting) of pavement is a major distress in flexible pavement. It is related to vehicles properties and/or pavement materials and conditions. This article presents an extensive experimental investigation in order to compare between the aggregate gradation according to Superpave and Marshall methods of asphalt concrete mix design on pavement rutting and to examine the sensitivity of rutting resistance to aggregate gradation. A wheel truck machine has been used for measurement of pavement rutting (permanent deformation). The tests were carried out at two controlled different air temperature 55℃ and 25℃. The results obtained showed that the adopting of aggregate gradation procedure of Superpave method of pavement mix design for Marshall method of asphalt concrete mix design can reduce the pavement rutting by about 50%. This achievement may be related to missing of three sieves in aggregate gradation procedure of Marshall method which controls rounded and finer aggregate particles. These sieves provide more continuity for aggregate gradation to ensure filling unnecessary gaps and produce more contact points between the aggregates in Hot Mix Asphalt (HMA). The outputs of the research support modifying Marshall method of asphalt concrete mix design by adopting aggregate gradation proposed in Superpave method. The results of study also showed that the coarser aggregate provided more resistance to pavement rutting.

Evaluation of rutting performance of stone matrix asphalt mixtures containing warm mix additives

Permanent deformation or rutting, one of the most important distresses in flexible pavements, has long been a problem in asphalt mixtures and thus a great deal of research has been focused on the development of a rheological parameter that would address the rutting susceptibility of both unmodified and modified bituminous binders. In this research, three warm mix additives(Sasobit, Rheofalt and Zycotherm) were used to modify 60-70 penetration grade base binder. The rutting potential of both modified and unmodified binders were evaluated through the multiple stress creep recovery(MSCR)-based parameter, nonrecoverable compliance(Jnr) and recovery parameter(R). Several performance tests carried on stone matrix asphalt(SMA) mixtures comprising different nominal maximum aggregate sizes(NMASs, 9.5, 12.5 and 19 mm), like Marshall stability, dynamic and static creep and Hamburg wheel tracking tests to evaluate their rutting performance. The objective of this work is to correlate MSCR test results to performance. Results indicate that for the range of the gradations investigated in this work, increasing the nominal maximum aggregate size of the gradation would increase the permanent deformation resistance of the SMA mixture. Addition of 3% sasobit to base binder leads an increase in Jnr100 about 82%. Addition of 2% rheofalt to base binder leads an recovery increase of about 9.76 % and 27.44% in stress levels of 100 and 3200 Pa, respectively. The results reveal that rutting resistance of mixtures improves as Jnr decreases. The use of the MSCR test in the rutting characterization of bituminous binders is highly recommended based on the results of this work.

Rutting Resistance of Asphalt Overlay with Multilayer Wheel Tracking Test

The rutting resistance of maltilayer asphalt overlay was researched by using laboratoty wheel tracking test. The effects of loading level and test temperature on rutting resistance of asphalt overlay structure were evahuaed by means of multilayer specimens . In comparison with multilayer tests, standard specimens of various layers were also conducted to evaluate the rutting resistance. Experimental results indicated that the test tempercature and applied load have a significant effect on rutting resistance of asphalt concrete. Higher test tempercature and heavier applied load resulted in higher rut depths. In addition, the mutilayer wheel tracking test has been demonstrated to be a more reasonable solution in evaluation on rutting resistance of asphatt pavement structure beasuse it reflects the cumulative permanent deformation in all of asphalt layers.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

复合涂层刀具加工RuT500的铣削力研究及参数优化

为探究和改善蠕墨铸铁RuT500的铣削力,基于单因素试验,选用TiAlN/AlCrN、TiCN/Al_(2)O_(3)复合涂层硬质合金刀具对RuT500进行铣削加工,结合响应面探究涂层和铣削参数对RuT500铣削力的影响规律,并进行了铣削参数优化。结果表明:相比于TiAlN/AlCrN复合涂层硬质合金刀具,TiCN/Al_(2)O_(3)复合涂层硬质合金刀具铣削RuT500时会产生更大的铣削力;对蠕墨铸铁铣削力影响最显著的因素是背吃刀量,其次是进给量,铣削速度对蠕墨铸铁铣削力的影响程度相对较少;铣削参数交互作用对铣削力的影响具有一定的显著性;取较大的背吃刀量a_(p)、适当的进给量f和较大的铣削速度v_(c)时,可以获得较低的铣削力F和良好的加工效率。

Impact of Intrinsic Properties of Aggregate and Volumetric Properties of Hot Mixture Asphalt (HMA) in the Influence of the Resistance to Rutting

Several studies show that properties of Hot Mixture Asphalt (HMA) mix design materials, aggregate gradation and volumetric properties had an influence on their resistance to rutting. However, these properties do not impact in the same way this performance. For a given aggregate type, an infinity aggregate gradation type can be observed, and for each type of HMA several types of bituminous binder can be used. This article aims to measure the evolution of resistance to rutting according to the three main classes of National Cooperative Highway Research Program (NCHRP) aggregate gradation (dense-graded, fine-graded and coarse-graded). To this end, a study was conducted on the measurement of rutting resistance for eight bituminous mixtures manufactured with two bitumen types and two types of manufacturing aggregates. The results showed that there is a priority order of these different parameters on the influence of the resistance to rutting. This highlights a competition between the properties of aggregate and type of granular skeleton. Indeed, for the same type of aggregate, asphalt binder type first impacts resistance to rutting of the HMA followed by aggregate gradation, volumetric properties of the mix and finally by the angularity of the aggregates. However, this order cannot be fixed and can depend of the intensity of each parameter.

Clinical implication of naive and memory T cells in locally advanced cervical cancer:A proxy for tumor biology and short-term response prediction

Background:This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally advanced cervical cancer(LACC).Methods:Thirty-four patients who received concurrent chemoradiotherapy and twenty-one patients who merely underwent radiotherapy were enrolled in this study.We retrospectively analyzed the T cell markers(i.e.,CD3,CD4,CD8),memory markers(i.e.,CD45,CCR7),and differentiation markers(i.e.,CD27)in the peripheral blood and tumor tissues of patients with LACC before treatment based on flow cytometry.We also analyzed the relationship of T cell subsets between peripheral blood and tumor tissues,and their correlation with complete response or partial response.Results:The percentage of central memory CD8^(+)TCM(CD8^(+)CD45RA^(−)CD27^(+)CCR7^(+))cells in LACC patients was significantly lower than that of the control group.The percentage of CD8^(+)TN in the peripheral blood of LACC patients was significantly higher than that of tumor tissues.CD8^(+)TEM in the peripheral blood was significantly lower than that of tumor tissues.The percentage of CD8^(+)TN and CD8^(+)TCM in human papillomavirus(HPV)positive samples was significantly higher than that of HPV-negative samples.Similarly,the percentage of CD8^(+)TCM in tumor tissues was significantly higher in cancer tissue samples with lymph nodes compared with those without.Conclusion:A higher proportion of CD4^(+)TCM and a lower proportion of CD8^(+)TN in the tumor microenvironment of LACC may contribute to the therapy response prediction.

Relating Cone Penetration and Rutting Resistance to Variations in Forest Soil Properties and Daily Moisture Fluctuations

Soil resistance to penetration and rutting depends on variations in soil texture, density and weather-affected changes in moisture content. It is therefore difficult to know when and where off-road traffic could lead to rutting-induced soil disturbances. To establish some of the empirical means needed to enable the “when” and “where” determinations, an effort was made to model the soil resistance to penetration over time for three contrasting forest locations in Fredericton, New Brunswick: a loam and a clay loam on ablation/ basal till, and a sandy loam on alluvium. Measurements were taken manually with a soil moisture probe and a cone penetrometer from spring to fall at weekly intervals. Soil moisture was measured at 7.5 cm soil depth, and modelled at 15, 30, 45 and 60 cm depth using the Forest Hydrology Model (ForHyM). Cone penetration in the form of the cone index (CI) was determined at the same depths. These determinations were not only correlated with measured soil moisture but were also affected by soil density (or pore space), texture, and coarse fragment and organic matter content (R2 = 0.54;all locations and soil depths). The resulting regression-derived CI model was used to emulate how CI would generally change at each of the three locations based on daily weather records for rain, snow, and air temperature. This was done through location-initialized and calibrated hydrological and geospatial modelling. For practical interpretation purposes, the resulting CI projections were transformed into rut-depth estimates regarding multi-pass off-road all-terrain vehicle traffic.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.