共查询到20条相似文献，搜索用时 0 毫秒
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Alireza TABARSA Nima LATIFI Abdolreza OSOULI Younes BAGHERI 《Frontiers of Structural and Civil Engineering》2021,15(2):520-536
This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters. 相似文献
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Hai-Bang LY Thuy-Anh NGUYEN Binh Thai PHAM May Huu NGUYEN 《Frontiers of Structural and Civil Engineering》2022,16(8):990
This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided. 相似文献
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Hai-Van Thi MAI May Huu NGUYEN Son Hoang TRINH Hai-Bang LY 《Frontiers of Structural and Civil Engineering》2023,17(2):284
Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS. 相似文献
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With the growing usage of supplementary cementitious materials (silica fume, fly ash, and ground blast furnace slag, etc.) in concrete, accurate prediction of green concrete compressive strength (CS) has become an issue worth investigating. In this paper, a green concrete CS prediction model was developed using hybrid artificial neural network with genetic algorithm (GA-ANN) based on 2479 green concrete CS experiment results. Also two prediction models based on support vector regression (SVR) and ANN algorithm were developed for comparison. Five new parameters were constructed based on the original nine influencing parameters of CS, taking all 14 parameters as input variables, the influence of the constructed parameters on the model performance was studied. Feature selection was then performed based on the maximum information correlation (MIC) and sensitivity analysis results, which could improve the model by deleting several parameters. The results showed that the prediction performance of the SVR, ANN, and GA-ANN models were improved after adding new parameters, the GA-ANN model has the best prediction performance. The accuracy and robustness of the GA-ANN model were effectively improved by deleting the input variables with lower MIC and sensitivity values. 相似文献
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This study explores the ability of various machine learning methods to improve the accuracy of urban water demand forecasting for the city of Montreal (Canada). Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine (ELM) models, in addition to a traditional model (Multiple linear regression, MLR) were developed to forecast urban water demand at lead times of 1 and 3 days. The use of models based on ELM in water demand forecasting has not previously been explored in much detail. Models were based on different combinations of the main input variables (e.g., daily maximum temperature, daily total precipitation and daily water demand), for which data were available for Montreal, Canada between 1999 and 2010. Based on the squared coefficient of determination, the root mean square error and an examination of the residuals, ELM models provided greater accuracy than MLR, ANN or SVR models in forecasting Montreal urban water demand for 1 day and 3 days ahead, and can be considered a promising method for short-term urban water demand forecasting. 相似文献
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分析了水泥掺量、水胶比和膨润土与黏土掺量对塑性混凝土无侧限抗压强度的影响,试验结果表明,塑性混凝土无侧限抗压强度随水泥掺量的增加而增大;随着水胶比的增加,强度有明显减小;随着膨润土与黏土的掺入比例增加,塑性混凝土的抗压强度有一定降低。 相似文献
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康建红 《四川建筑科学研究》2011,37(1)
人工神经网络技术由于自组织、自学习、自适应的能力,常被引入灰色系统模型建立、模式识别、目标分类等研究领域.本次研究运用人工神经网络技术,选取土聚水泥碱激发体系中的碱激发剂浓度(COH-)、碱硅摩尔比(M2O/SiO2)和硅摩尔比(Al2O3/SiO2)为预测指标,基于 MATLAB 神经网络工具箱,建立预测方法,对土聚水泥的 28 d抗压强度进行预测.结果表明,预测精度较高. 相似文献
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准确地预测出混凝土材料在使用过程的实时强度对于正确评估结构安全性有着重要的意义。影响混凝土材料实时强度的主要因素包括龄期、环境类别、水灰比、胶凝材料用量等等。采用人工神经网络进行混凝土实时强度影响因素敏感性分析。首先,对影响混凝土实时强度的各类因素进行分析,确定敏感性因素。其次,针对龄期敏感性因素,建立两个神经网络,一个神经网络的输入变量包含龄期,另一个不包含龄期,将训练好的两个神经网络用同组数据进行测试,比较两组测试结果,以此来确定龄期因素对混凝土强度的敏感性。采用上述方法分别对环境类别、水灰比、胶凝材料用量等因素进行敏感性分析。最后,通过比较确定混凝土龄期、环境类别、水灰比为影响混凝土实时强度的敏感性因素。 相似文献
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利用神经网络的反馈分析方法及其在地下厂房中的应用 总被引：1，自引：1，他引：1
将反馈分析问题归结为约束最优化问题,采取最为直接的正演反分析的求解方案,即对结构进行正向数值分析以实现约束条件,利用人工神经网络进行参数反演以满足目标函数,从而建立基于神经网络的反馈分析方法,并给出详细实现步骤。该方法分析过程简单,通用性强。以溪洛渡水电站左岸地下厂房洞室群开挖过程的反馈分析为例,根据工程施工期位移监测资料,以三维连续介质快速Lagrange分析程序FLAC3D作为数值分析软件,建立神经网络数值反馈分析系统。围岩位移反馈分析成果与实测数据吻合,后期开挖的围岩变形、应力以及支护结构受力等的预测成果合理,这可作为溪洛渡厂房洞室群的围岩稳定性评价的可靠基础,也证明该方法是解决大型复杂工程监测反馈分析问题的有效途径,可能得到进一步的推广和应用。 相似文献
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In concrete structures design, the compressive strength of circular concrete columns confined by spiral stirrups is an important mechanical property in evaluating the performance of concrete structures. However, evaluating the compressive strength of confined concrete columns is rich in challenge due to the complex mechanics between the concrete and the transverse reinforcements. The objective of this paper is to establish an artificial neural network (ANN) model to evaluate the compressive strength of concrete columns confined by transverse reinforcements. The model proposed in this study is suitable for both normal‐strength and high‐strength concrete columns, covering concrete strengths were in the range of 19.1–151 MPa. Three main influential parameters, including the tensile yield strength and the volumetric ratio of the transverse reinforcements, as well as the concrete strength, were applied as input variables to the model. The ANN model was trained and tested by a reliable database consisting of 240 data sets obtained from authors and published literature. The proposed ANN model used to predict the compressive strength of circular concrete columns confined by spiral stirrup had high applicability and reliability compared with existing analytical models. 相似文献
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人工神经网络技术在高强混凝土中的应用--配合比设计初探 总被引：5，自引：1，他引：4
探讨了在多因素影响下,人工神经网络技术在混凝土配合比设计方面的实现手段．采用以正交设计试验作为学习样本模拟真实系统的方法,来模拟完全试验;同时,以部分试验数据为研究对象,通过自组织神经网络分类计算,构成学习样本来模拟真实系统,也得到了较为满意的结果．此项研究除提供了人工智能在混凝土配合比设计中的应用方法外,还在具体研究问题的背景下,为神经网络理论在确立学习样本的方法上寻求了一个可行的途径 相似文献
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人工神经网络技术综合考虑了掺活化煤矸石混凝土强度的各种影响因素,可用于预测混凝土强度.选取了掺活化煤矸石粉混凝土配料中7个主要因素作为输入值,混凝土28d强度作为输出值,建立起混凝土强度预测BP网络模型,进而对掺活化煤矸石配合比强度试验数据进行分析预测,效果良好.结果表明该方法用于掺矿物掺合料混凝土强度预测方面是可行的. 相似文献
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塑性混凝土强度试验研究 总被引：1，自引：0，他引：1
本文给出了塑性混凝土立方体抗压强度、劈裂抗拉强度和抗剪强度的测定方法,试验的关键是要注意选择适当的测量量程以及加荷速度.在大量试验的基础上对影响塑性混凝土抗压强度、抗拉强度和抗剪强度的因素进行了详细的分析,并得出了各因素对塑性混凝土各种强度的影响规律.所得结论对塑性混凝土配合比设计及施工具有较重要的指导意义. 相似文献
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High-performance concrete (HPC) as a highly sophisticated aggregate in constructional projects has made modeling given mechanical properties a very complex problem. Declaring by many studies, mechanical features of HPC are not only characterized by the maximum size of coarse aggregate and water amount since influencing by the other components. Using fly-ash and silica fume as the key constituents can simultaneously increase the hardness aspects and the environmental effects. Considering the compressive strength and slump flow of concrete should be investigated before performing any practical practices. Artificial intelligence approaches with precise and low-cost methods can replace the costly experimental ways. Therefore, the present paper has aimed to link a prediction model with optimization algorithms to accurately appraise the hardness properties of HPC samples rarely found in literature like this way. In this regard, a machine learning approach of Support Vector Regression using two kernels of Gaussian and radial basis function is coupled with matheuristic algorithms to optimize the modeling process of compressive strength and slump flow of HPC samples. The internal settings of SVR would be tuned at optimal rate by optimizers to function efficiently. To investigate the performance of hybrid frameworks developed in this research, several indicators evaluated the results of hybrid models. Therefore, the R2 of the models was calculated averagely at 0.91 with a maximum difference rate of 11% for the testing phase. While the RMSE index assessed the models with higher values of 16.56 mm for slump and 12.86 MPa for compressive strength. Generally, using smart approaches with high-accuracy performance has been proposed to be used instead of physical procedures increasing the productivity of concrete compressive strength in terms of time, energy, and cost criteria. 相似文献
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