The influences of natural sand, manufactured-sand （MS） and stone-dust （SD） in the manufactured-sand on workability, compressive strength, elastic modulus, drying shrinkage and creep properties of high-strength con...The influences of natural sand, manufactured-sand （MS） and stone-dust （SD） in the manufactured-sand on workability, compressive strength, elastic modulus, drying shrinkage and creep properties of high-strength concrete （HSC） were tested and compared. The results show that the reasonable content （7%-10.5%） of SD in MS will not deteriorate the workability of MS-HSC. It could even improve the workability. Moreover, the compressive strength increases gradually with the increasing SD content,and the MS- HSC with low SD content （smaller than 7%） has the elastic modulus which approaches that of the natural sand HSC, but the elastic modulus reduces when the SD content is high. The influence of the SD content on drying shrinkage performance of MS-HSC is closely related to the hydration age. The shrinkage rate of MS-HSC in the former 7 d age is higher than that of the natural sand HSC, but the difference of the shrinkage rate in the late age is not marked. Meanwhile the shrinkage rate reduces as the fly ash is added; the specific creep and creep coefficient of MS-HSC with 7% SD are close to those of the natural sand HSC.展开更多
Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding o...Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.展开更多
Plastic concrete is an engineering material,which is commonly used for construction of cut-offwalls to prevent water seepage under the dam.This paper aims to explore two machine learning algorithms including artificia...Plastic concrete is an engineering material,which is commonly used for construction of cut-offwalls to prevent water seepage under the dam.This paper aims to explore two machine learning algorithms including artificial neural network (ANN)and support vector machine (SVM)to predict the compressive strength of bentonite/sepiolite plastic concretes.For this purpose,two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data)were prepared by conducting an experimental study.The results confirm the ability of ANN and SVM models in prediction processes.Also,Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength,respectively.In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount)and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE)of model, respectively.Finally,the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.展开更多
Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and varia...Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.展开更多
Numerical analysis of the optimal supporting time and long-term stability index of the surrounding rocks in the underground plant of Xiangjiaba hydro-power station was carried out based on the rheological theory. Firs...Numerical analysis of the optimal supporting time and long-term stability index of the surrounding rocks in the underground plant of Xiangjiaba hydro-power station was carried out based on the rheological theory. Firstly,the mechanical parameters of each rock group were identified from the experimental data; secondly,the rheological calculation and analysis for the cavern in stepped excavation without supporting were made; finally,the optimal time for supporting at the characteristic point in a typical section was obtained while the creep rate and displacement after each excavation step has satisfied the criterion of the optimal supporting time. Excavation was repeated when the optimal time for supporting was identified,and the long-term stability creep time and the maximum creep deformation of the characteristic point were determined in accordance with the criterion of long-term stability index. It is shown that the optimal supporting time of the characteristic point in the underground plant of Xiangjiaba hydro-power station is 5-8 d,the long-term stability time of the typical section is 126 d,and the corresponding largest creep deformation is 24.30 mm. While the cavern is supported,the cavern deformation is significantly reduced and the stress states of the surrounding rock masses are remarkably improved.展开更多
为了研究桩撑支护的深基坑在开挖过程中混凝土支撑的轴力特性,本文以珠海地区深基坑为典型案例,分析了基坑开挖过程中支撑轴力监测值的时序特征,研究了计算值与监测值之间的关系。针对轴力现场监测值超过警戒值、基坑未开挖时轴力持续...为了研究桩撑支护的深基坑在开挖过程中混凝土支撑的轴力特性,本文以珠海地区深基坑为典型案例,分析了基坑开挖过程中支撑轴力监测值的时序特征,研究了计算值与监测值之间的关系。针对轴力现场监测值超过警戒值、基坑未开挖时轴力持续增加等情况,从荷载、温度、徐变和收缩4个方面进行了分析,揭示了基坑在开挖过程中支撑轴力的演化机制。研究表明:(1)随着基坑开挖深度的增加,支撑轴力均表现出增大的趋势;基坑开挖到底后,第1、2层支撑轴力现场监测值是理论计算值的1.67~3.52倍。(2)温度对轴力有明显的影响,达68 k N/℃;收缩及徐变的影响更大,约为轴力现场监测值的1/3。(3)根据拆撑前后的实测数据,切断支撑消除外荷载后,应力计仍能测到轴力,约为未切断前轴力的60%。本文研究结果可为深基坑支撑设计、施工和监测提供参考。展开更多
基金the National West Communication Construction Technology Project(No.200331881106)
文摘The influences of natural sand, manufactured-sand （MS） and stone-dust （SD） in the manufactured-sand on workability, compressive strength, elastic modulus, drying shrinkage and creep properties of high-strength concrete （HSC） were tested and compared. The results show that the reasonable content （7%-10.5%） of SD in MS will not deteriorate the workability of MS-HSC. It could even improve the workability. Moreover, the compressive strength increases gradually with the increasing SD content,and the MS- HSC with low SD content （smaller than 7%） has the elastic modulus which approaches that of the natural sand HSC, but the elastic modulus reduces when the SD content is high. The influence of the SD content on drying shrinkage performance of MS-HSC is closely related to the hydration age. The shrinkage rate of MS-HSC in the former 7 d age is higher than that of the natural sand HSC, but the difference of the shrinkage rate in the late age is not marked. Meanwhile the shrinkage rate reduces as the fly ash is added; the specific creep and creep coefficient of MS-HSC with 7% SD are close to those of the natural sand HSC.
文摘Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.
文摘Plastic concrete is an engineering material,which is commonly used for construction of cut-offwalls to prevent water seepage under the dam.This paper aims to explore two machine learning algorithms including artificial neural network (ANN)and support vector machine (SVM)to predict the compressive strength of bentonite/sepiolite plastic concretes.For this purpose,two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data)were prepared by conducting an experimental study.The results confirm the ability of ANN and SVM models in prediction processes.Also,Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength,respectively.In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount)and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE)of model, respectively.Finally,the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.
基金Acknowledgements This research was supported by the Research Program funded by Seoul National University of Science and Technology(SeoulTech).
文摘Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.
基金Projects(50911130366, 50979030) supported by the National Natural Science Foundation of ChinaProject(2008BAB29B01) supported by the National Key Technology R&D Program of China
文摘Numerical analysis of the optimal supporting time and long-term stability index of the surrounding rocks in the underground plant of Xiangjiaba hydro-power station was carried out based on the rheological theory. Firstly,the mechanical parameters of each rock group were identified from the experimental data; secondly,the rheological calculation and analysis for the cavern in stepped excavation without supporting were made; finally,the optimal time for supporting at the characteristic point in a typical section was obtained while the creep rate and displacement after each excavation step has satisfied the criterion of the optimal supporting time. Excavation was repeated when the optimal time for supporting was identified,and the long-term stability creep time and the maximum creep deformation of the characteristic point were determined in accordance with the criterion of long-term stability index. It is shown that the optimal supporting time of the characteristic point in the underground plant of Xiangjiaba hydro-power station is 5-8 d,the long-term stability time of the typical section is 126 d,and the corresponding largest creep deformation is 24.30 mm. While the cavern is supported,the cavern deformation is significantly reduced and the stress states of the surrounding rock masses are remarkably improved.
文摘为了研究桩撑支护的深基坑在开挖过程中混凝土支撑的轴力特性,本文以珠海地区深基坑为典型案例,分析了基坑开挖过程中支撑轴力监测值的时序特征,研究了计算值与监测值之间的关系。针对轴力现场监测值超过警戒值、基坑未开挖时轴力持续增加等情况,从荷载、温度、徐变和收缩4个方面进行了分析,揭示了基坑在开挖过程中支撑轴力的演化机制。研究表明:(1)随着基坑开挖深度的增加,支撑轴力均表现出增大的趋势;基坑开挖到底后,第1、2层支撑轴力现场监测值是理论计算值的1.67~3.52倍。(2)温度对轴力有明显的影响,达68 k N/℃;收缩及徐变的影响更大,约为轴力现场监测值的1/3。(3)根据拆撑前后的实测数据,切断支撑消除外荷载后,应力计仍能测到轴力,约为未切断前轴力的60%。本文研究结果可为深基坑支撑设计、施工和监测提供参考。
