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Yasmina KELLOUCHE Mohamed GHRICI Bakhta BOUKHATEM 《Frontiers of Structural and Civil Engineering》2021,15(3):793
Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO2 concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete. 相似文献
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以BP人工神经网络模型为基础,建立预测模型,以小区某栋建筑物l期~8期的沉降观测数据为输入数据和输出数据,对网络模型进行训练,并对9期~12期实际观测值与预测值进行了比较,结果比较理想,从而验证了采用BP人工神经网络模型进行建筑物沉降的预测是可行的。 相似文献
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Bashar Tarawneh 《Soils and Foundations》2013,53(4):607-615
Over the last few years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, the ability to accurately predict pile setup may lead to more economical pile design, resulting in a reduction in pile length, pile section, and size of driving equipment. In this paper, an ANN model was developed for predicting pipe pile setup using 104 data points, obtained from the published literature and the author's own files. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum ANN model.Finally, the paper compares the predictions obtained by the ANN with those given by a number of empirical formulas. It is demonstrated that the ANN model satisfactorily predicts the measured pipe pile setup and significantly outperforms the examined empirical formulas. 相似文献
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在分析岩爆主要影响因素的基础上,建立了基于BP神经网络岩爆预测模型,采用已有岩爆发生数据作为训练样本对网络进行训练,利用收敛的网络进行岩爆烈度预测,预测结果与实际吻合,说明利用人工神经网络预测岩爆发生烈度是一种可行的方法。 相似文献
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Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm 总被引:1,自引:0,他引:1
Ebrahim Ebrahimi Masoud Monjezi Mohammad Reza Khalesi Danial Jahed Armaghani 《Bulletin of Engineering Geology and the Environment》2016,75(1):27-36
In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy. 相似文献
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《Structure and Infrastructure Engineering》2013,9(5):481-495
Finite element (FE) model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. FE model updating method based on the hybrid genetic algorithm, by combining genetic algorithm and the modified Nelder–Mead's simplex method, is presented to improve bridge structures' FE model. An objective function is formulated as a linear combination of fitness functions on natural frequencies, mode shapes and static deflections using measurements and analytical results to update both stiffness and mass simultaneously. A commercial FE analysis tool, which can utilise previously developed element library and solution algorithms, is adopted for applications on diversified and complex structures. The validity of the proposed method is verified by using a simply supported bridge model with three I-shaped girders. FE models such as grid, beam-shell and shell model are considered to modify initial FE models on the experimental structure. Experimental results suggest that the proposed method can be applied efficiently to various FE models and is feasible and effective when this method is applied to identify FE modelling errors. 相似文献
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混凝土抗裂性能评价与预测一直是学术界与工程界的研究难点,常规的预测模型主要基于某几项指标,形式因个人的理解不同而各异。一种仿生模型——人工神经网络则能很好地解决这个难题,试验尝试用BP人工神经网络对多种配后比的混凝土进行抗裂性能评价与预测,结果表明此模型的可靠度很高,效果良好。该方法用于掺矿物掺和料混凝土抗裂性能预测方面是可行的。 相似文献
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Pei Huafu Meng Fanhua Zhu Honghu 《Bulletin of Engineering Geology and the Environment》2021,80(10):7403-7422
Bulletin of Engineering Geology and the Environment - Accurate landslide displacement prediction is essential for an early warning system. At present, the inputs of the data-driven models adopted... 相似文献
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In the present contribution, operational modal analysis in conjunction with bees optimization algorithm are utilized to update the finite element model of a solar power plant structure. The physical parameters which required to be updated are uncertain parameters including geometry, material properties and boundary conditions of the aforementioned structure. To determine these uncertain parameters, local and global sensitivity analyses are performed to increase the solution accuracy. An objective function is determined using the sum of the squared errors between the natural frequencies calculated by finite element method and operational modal analysis, which is optimized using bees optimization algorithm. The natural frequencies of the solar power plant structure are estimated by multi-setup stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. The proposed algorithm is efficiently implemented on the solar power plant structure located in Shahid Chamran university of Ahvaz, Iran, to update parameters of its finite element model. Moreover, computed natural frequencies by numerical method are compared with those of the operational modal analysis. The results indicate that, bees optimization algorithm leads accurate results with fast convergence. 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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Fangyu LIU Wenqi DING Yafei QIAO Linbing WANG 《Frontiers of Structural and Civil Engineering》2020,14(6):1299
The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers. 相似文献
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Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R2) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements. 相似文献
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针对火灾探测的特点,将模糊系统和神经网络有机结合,实现模糊系统设计参数的自动调整。采用符合国家标准明火、阴燃火以及厨房环境下的干扰火等作为模糊神经网络的训练样本和测试样本,依据模糊神经网络算法要求,完成了网络结构的设计,并给出相应的计算模型,利用微粒群算法对网络的权值进行学习与训练。结果表明,该算法在探测国家标准火的火灾状态方面具有有效性和可行性。 相似文献
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Fikret Kocabaş Özgür Kişi Mehmet Ardiçlioğlu 《Civil Engineering and Environmental Systems》2013,30(4):367-375
Density differences may occur because of temperature differentials, suspended sediments, dissolved salts or other chemicals. Most of the large surface reservoirs are stably stratified throughout most, or all, of the year. One of the means of assisting the management is to allow a selective withdrawal from the reservoir. And while an intake is used for withdrawal (from the lower layer), a maximum discharge is required not allowing the uptake of the upper layer fluids. The value of the intake's vertical distance from the upper layer elevation (submergence) when the upper layer fluids begin to be drawn into the intake is known as ‘critical submergence’. In this study, the critical submergence for a circular intake pipe in a stratified body (which has different layer thickness) is investigated. Experiments were conducted on a vertically flowing downward intake pipe in a still-water reservoir. Artificial neural network (ANN) models and formulas, which are found by the theoretical analysis of critical spherical sink surface (CSSS), are used for the analysis of experimental results. The CSSS has the same centre and discharge as the intake. The ANN model and CSSS results are compared with the experimental results. 相似文献
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This paper presents a self-adaptive sensor fault detection and diagnosis (FDD) strategy for local system of air handing unit (AHU). This hybrid strategy consists of two stages. In the first stage, a fault detection model for the AHU control loop including two back-propagation neural network (BPNN) models is developed. BPNN models are trained by the normal operating data of system. Based on sensitive analysis for the first BPNN model, the second BPNN model is constructed in the same control loop. In the second stage, a fault diagnosis model is developed which combines wavelet analysis method with Elman neural network. The wavelet analysis is employed to process the measurement data by extracting the approximation coefficients of sensor measurement data. The Elman neural network is used to identify sensor faults. A new approach for increasing adaptability of sensor fault diagnosis is presented. This approach gains clustering information of the approximations coefficients by fuzzy c-means (FCM) algorithm. Based on cluster information of the approximation coefficients, the unknown sensor fault can be identified in the control loop. Simulation results in this paper show that this strategy can successfully detect and diagnose fixed biases and drifting fault of sensors for the local system of AHU. 相似文献
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《Planning》2022,(3)
水产养殖池塘是一个多变量、非线性和大时延系统,其中溶解氧的预测也是一个复杂的问题。针对大连某水产养殖池塘,作者建立了一个基于Levenberg-Marquardt(LM)神经网络和遗传算法(GA)的溶解氧预测模型GA-LM,并将该模型与传统的BP神经网络进行比较分析。结果表明:使用本研究中建立的GA-LM模型预测的溶解氧值和实际测定值吻合较好,预测更为精准,运行时间明显减少。 相似文献