Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
Development of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete am1, Thuy-Anh Nguyen1, May Huu Nguyen; Civil Engineering Department, University’ of Transport Technology, 54 Trieti Khuc, Thanh Xuan. Hanoi 100000, Vietnam•Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering. Hiroshima University. I-4-Ỉ. Kagamiyama. Higas Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete hi-Hiroshima, Hiroshima 739-8527, Japan*Corresponding authorsEmail addresses: banglhih utt.edu.vn (H.-B. Ly). binhpt'rt utt.edu.vn (B. p. Pham).anhntiDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
d utt.edu.vn (T.-A. Nguyen), and nauyenhuumavffl hiroshima-u.ac.jp (M. H. Nguyen)Abstract:This study investigated the usability of an artificial neuraDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete odel was developed using an experimental database of 115 samples obtained from various sources considering nine key factors of see. The validation of the proposed model was evaluated via six indices including correlation coefficient, mean squared error, mean absolute etTor. IA. Slope, and mean1Elect Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete ronic copy available at. https://ssm.com/abstract=3970696absolute percentage error. In addition, the importance of each parameter affecting the compreDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
ssive strength of see was investigated utilizing partial dependence plots. The findings demonstrated that the proposed ANN-GWO model is a reliable preDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete Artificial Neural Network (ANN); Grey Wolf optimizer (GWO) algorithm; compressive strength; self-compacting concrete;1.IntroductionIn the sphere of construction and building, concrete is the most often used material due to its ease of production, low cost, and valuable structure characteristics [ 1. Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete 2]. It may be used in a broad variety of structures such as buildings, bridges, roads, and dams. In line with the scientific growth path, the need forDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
high-performance concrete is developing on a continuous basis. As a result, several particular concrete types have been proposed with notable featureDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete crete (SCC). a concrete type that can approach and fill the corners of formwork without the requirement for a compaction phase [6,7]. Since then, various studies have been focused on developing the applications of this kind of concrete [8.9]. On the one hand, see is listed as a kid of high-performan Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete ce concrete, flexible deformability, good segregation resistance, and less blocking surrounding the reinforcement. The exclusion of the compaction steDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
p2Electronic copy available at. https://ssm.com/abstract=3970696bl ings several advantages of see. including economic efficiency (e.g., accelerated caDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete 6.10 13J.On the other hand. Io achieve its desired flowable behaviors and proper mechanical properties, see requires a complex manipulation of several mixture variables [10.1 I J. For instance, the walcr-lo-bmdcr (w/b) ratio of see is lower than conventional concrete, which is usually supported by s Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete pecial additives and superplasticizers to obtain the desired workability [14-17]. Also, the grading of the aggregates, including aggregate shapes, texDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
ture, mineralogy, and strength, are always carefully considered to ensure workability and concrete strengths [18.19]. These features lead to a signifiDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete out the need for predicting the properties of sec in both the fresh and hardened stages. The traditional applications of analytical models to represent the influence of each of these parameters on the properties of see. and then optimizing this model utilizing regression analysis. However, so far. n Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete o explicit equations have been established due to these methods being less productive for nonlincarly separable data and complicated [2I.22J.In this rDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
egard, over the past few decades, various modeling methods utilizing artificial intelligence (Al) techniques have been adopted, such as artificial neuDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete 25]. Among these. ANN is a more prevalent and efficient approach since its ability to classify to capture interrelationships among input-3Electronic copy available at. https://ssm.com/abstract=3970696output data pairs. Numerous researchers have proposed their own ANN models for predicting the concre Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete te strength [26-28]. Regarding see, several models have also been presented for predicting the compressive strength [29-31]. Yeh has soon demonstratedDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
the opportunities of adapting ANN to predict high-performance concrete's compressive strength [29]. The viability of utilizing ANNs to forecast the cDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete were collected from the previous studies and employed for training and evaluating the proposed model. Siddique et al. presented the useability of neural network for predicting the compressive strength of see based on some input properties [31]. Their proposed model could be easily extended to diffe Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete rent input parameters of the experimental results, containing bottom ash as a replacement of sand. Despite this, there has not been a detailed investiDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
gation into an improved ANN model for predicting the compressive strength of see. The need for a novel, appropriate artificial neural network model toDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete arch, the artificial neural network (ANN) approach coupled with the Grey Wolf optimizer (GWO) algorithm for forecasting the compressive strength of see is examined. For this target, a variety of databases from different independent sources was gathered and employed to train and assess the proposed m Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete odel. The ANN model is established on the basis of two groups of input parameters, including concrete mixture components (i.e., the contents of binderDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
, fine and coarse aggregates, superplasticizer and water-to-binder ratio), and the fresh properties see such as slump4Electronic copy available at. htDevelopment of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete used parameters on the compressive strength of see was then discussed.2.Materials and methods2.1.Machine learning methods2.1.1.Artificial Neural Network (ANN)Artificial Neural Network (ANN) is being widely used to solve prediction problems by drawing on biology’s understanding of how the nervous sys Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete tem functions [32-35]. ANN contains many simple processing elements, the so-called neurons. An ANN is made up of nodes and linked parts that are dividDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
ed into three layers: the input layer, hidden layer, and output layer. Because of this training process, the neural network produces a model that can Development of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai Pha Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete the training progress. A linking between nodes carries a weighted representative of some earlier learning stage. On the basis of the changes in weights, the input-output correlation could be established. The system has to be educated to recreate the input-output correlation, which is called optimal Development of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete weights [37.38]. In an ANN model, the correlation between the input and output variables is determined by the collected data points. Because they areDevelopment of articial intelligence based model for prediction of the compressive strebgth of self compacting concrete
ver}' independent of one another, it is feasible to execute a large number of processes at the same time.5Development of artificial intelligence-based model for prediction of the compressive strength of self-compacting concreteHai-Bang Ly1’*, Binh Thai PhaGọi ngay
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