Artificial intelligence-based estimation of ultra-high-strength concrete's flexural property

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength.

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Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.

https://www.mdpi.com/1996-1073/13/9/2407/pdf

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R2 value with fewer errors, producing higher precision than other models with lesser R2 values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength.

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Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R2), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

/pdf/evaluating-the-strength-and-impact-of-raw-ingredients-of-3no1do79.pdf

Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods

Line loss rate plays an essential role in evaluating the economic operation of power systems. However, in a low voltage (LV) distribution network, calculating line loss rate has become more cumbersome due to poor configuration of the measuring and detecting device, the difficulty in collecting operational data, and the excessive number of components and nodes. Most previous studies mainly focused on the approaches to calculate or predict line loss rate, but rarely involve the evaluation of the prediction results. In this paper, we propose an approach based on a gradient boosting decision tree (GBDT), to predict line loss rate. GBDT inherits the advantages of both statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. An empirical study on a data set in a city demonstrates that our proposed approach performs well in predicting line loss rate, given a large number of unlabeled examples. Experiments and analysis also confirmed the effectiveness of our proposed approach in anomaly detection and practical project management.

Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree

The invention discloses a distribution equipment condition monitoring system based on the Internet of Things technology and an implementation method thereof, relates to the technical field of online monitoring of power equipment, and can simultaneously promote the technical fusion of an intelligent power grid, the Internet of Things and cloud computing, so that users can quickly develop and upgrade and maintain own distribution equipment. A distribution equipment state monitoring cloud platform is designed according to a three-tier architecture system of the Internet of Things technology, andthe distribution equipment state monitoring cloud platform comprises a field sensing layer, a network communication layer and an application service layer; the tail end of the distribution equipment state monitoring cloud platform adopts an intelligent electric meter and a sensor to collect operation data and environmental data of the distribution equipment in real time, and data transmission is realized through a public GPRS wireless network; and a monitoring object of the distribution equipment state monitoring cloud platform comprises an outdoor switching station, a box-type transformer, aspecial transformer, a distribution ring main unit and a distribution terminal cabinet, and can provide advanced functions of online operation monitoring, asset management and big data analysis for the distribution equipment.

Distribution equipment condition monitoring system based on Internet of Things technology and implementation method thereof

The invention relates to the technical field of distribution network line loss calculation, and discloses a distribution network theoretical line loss calculation method based on a line loss load characteristic curve. According to the method, a line loss load characteristic curve is drawn, a relationship between a load rate and a line loss rate is established, and rise and fall of distribution network line loss can be learnt through the line loss load characteristic curve, so that the method is highly reliable. The method comprises steps of simplifying a distribution network line model, then performing load flow calculation, reading load data and line information, then calculating distribution network line loss, calculating power loss of each line and power loss of the whole network, thendrawing a line loss load characteristic curve, establishing a relationship between the load rate or line headgate power and line loss rate, and finally, analyzing the line loss load characteristic curve.

Distribution network theoretical line loss calculation method based on line loss load characteristic curve

The invention discloses a power distribution network theoretical line loss precise delimiting method. The method comprises five steps of determining a calculation object, establishing a calculation model, constructing an extreme value model, solving an extreme value point set and drawing upper and lower bound curves. The method determines theoretical line loss upper and lower bound curves of a power distribution network by adopting a theoretical line loss calculation model based on optimal power flow, and only line network parameters and feeder line head end electric quantity data are needed in the whole calculation process, so that the problem that it is difficult for a power distribution system to obtain operation parameters in real time is avoided, and a feasible theoretical basis is provided for theoretical line loss anomaly evaluation; and the method has wide applicability and high practical value.

Yao Mengting

Papers

Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree

Distribution equipment condition monitoring system based on Internet of Things technology and implementation method thereof

Distribution network theoretical line loss calculation method based on line loss load characteristic curve

Power distribution network theoretical line loss precise delimiting method