应用人工神经网络预测建筑物空调负荷

用VB编制了人工神经网络的通用BP算法程序。根据西安参考年气象参数，采用动态模拟程序计算了菜办公楼4月至9月逐时冷负荷，结果显示利用神经网络的预测值与计算值吻合。     

人工神经网络在商场建筑物冷负荷预测中的应用

对空调系统进行冷负荷预测是对冰蓄冷系统进行优化控制的重要前提与基础。经过对各种预测方法的结果比较 ,人们发现人工神经网络预测的结果更接近实际值。基于人工神经网络的通用BP( Back Propagation)算法编制的程序 ,实际对一栋采用冰蓄冷空调的商场性质建筑物进行冷负荷预测并加以评价。此程序采用 Visual Basic编制 ,含有 7个输入层以及 1个输出层 ,利用通用 BP算法。结果显示利用人工神经网络预测建筑物冷负荷比较可靠。     

人工神经网络在空调系统负荷预测中的应用

采用理论分析的方法,通过分析国内外在该方面的研究成果,剖析了人工神经网络在空调系统负荷预测中的应用,指出了利用人工神经网络（ANN）具有的高度的并行处理和可完成复杂的输入输出的非线性映射能力,进行空调系统负荷预测精度高、准确度好。ANN是一种有效的空调负荷预测手段。     

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificial neural network

In the recent era, piled raft foundation (PRF) has been considered an emergent technology for offshore and onshore structures. In previous studies, there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study. Finite element (FE) models are prepared with various design variables in a double-layer soil system, and the load sharing and interaction factors of piled rafts are estimated. The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neural network (ANN) modeling, and some prediction models are proposed. ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factors through backpropagation technique. The factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be used for developing the design strategy of PRF.     

Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network

Pre-stressed precast high strength concrete (PHC) nodular piles with hyper-MEGA construction method are favorably used in medium to high-rise building foundations. In this study, a feed-forward neural network (FFNN) was adopted to investigate the ultimate axial load bearing capacity of the PHC nodular pile. The network receives the composite pile and geotechnical conditions with eight input neurons and outputs the nodular pile's ultimate axial load bearing capacity. Among numerous possible FFNN network architectures, the most accurate one is determined by optimizing the hidden layer. Network training is conducted with Bayesian regularization backpropagation (BRB); the training datasets consist of static pile load test and standard penetration test index of soil profile collected from various projects in Vietnam. The significance of each input parameter is quantified with importance-based sensitivity analysis. An explicit function has been constructed from weights and bias values at each neuron in the FFNN to estimate the axial load bearing capacity. The excellent agreement of all output values by the proposed FFNN with the measured values proved the model’s robustness and reliability. The predictive capacity of the proposed FFNN model has significantly outperformed all current empirical formulas. The outcome of this study can be directly put into engineering practice to furnish an economically optimal design of the composite nodular pile.     

基于人工神经网络的复合地基沉降预测

利用人工神经网络强大的非线性映射和学习能力,提出了基于人工神经网络的复合地基沉降预测新方法.该方法利用实测资料直接建模,避免了传统方法计算过程中各种人为因素的干扰,所建立的模型预测精度高、简便易行,因此具有广泛的工程实用价值.     

Construction equipment productivity estimation using artificial neural network model

Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well‐known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non‐linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network's inherent ability to capture non‐linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non‐linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.     

基于CPTu数据的黏性土不排水抗剪强度#br# 神经网络预测

利用人工神经网络模型,建立基于孔压静力触探（CPTu）现场测试数据的黏性土不排水抗剪强度的预测方法。为建立和验证人工神经网络模型,在3个场地开展CPTu和十字板剪切现场测试,共取得33个测孔的CPTu试验数据和相对应的不排水抗剪强度实测值。通过对比分析不同输入向量、不同网络隐层数、不同神经元数及不同改进算法对人工神经网络模型性能的影响,确定人工神经网络模型的具体形式。通过对训练组数据开展机器学习,所建立的人工神经网络模型能够有效地基于CPTu获得的端阻力和孔隙水压力现场测试数据对黏土不排水抗剪强度进行预测,预测结果与十字板剪切试验实测结果非常接近。与传统用于估算不排水强度的经验关系相比,采用人工神经网络模型预测结果与实测结果相关性显著提高、误差明显降低。     

Prediction of roadheader performance by artificial neural network

Performance prediction of the roadheaders is one of the main subjects in determining the economics of the underground excavation projects. During the last decades, researchers have focused on developing performance prediction models for roadheaders. In the first stage of this study, the performance of a roadheader used in Kucuksu sewage tunnel (Istanbul) was recorded in detail and the instantaneous cutting rate (ICR) of the machine was determined. The uniaxial compressive strength (UCS) and rock quality designation (RQD) are used as input parameters in previously developed empirical models in order to point out the efficiency of these models, and the relationships between measured and predicted ICR for different encountered formations. In the second stage of the study, Artificial Neural Network (ANN) technique is used for predicting of the ICR of the roadheader. A data set including UCS, RQD, and measured ICR are established. It is traced that a neural network with two inputs (RQD and UCS) and one hidden layer can be sufficient for the estimation of ICR. In addition, it is determined that increase in number of neurons in hidden layer has positive optimizing on the performance of the ANN and a hidden layer larger than 10 neurons does not have a significant effect on optimizing the performance of the neural network. Furthermore, probability of memorizing is being recognized in this situation. Based on this study, it is concluded that the prediction capacity of ANN is better than the empirical models developed previously.     

大跨度煤棚主体结构设计风荷载的神经网络建模研究及应用

风荷载是大跨度煤棚结构设计中的主要控制荷载。随着结构抗风研究尤其是风洞试验数据的积累,结合数据挖掘进行结构智能化抗风设计是一种趋势。基于701组工况4581个柱面及球面屋盖风洞试验样本进行数据挖掘和统计分析,建立了脉动风荷载参数的广义回归神经网络预测模型;通过对12480个工况的单、双层柱面及球面网壳结构进行参数化风振响应分析,总结了等效静风荷载的经验表达式;建立了基于人工神经网络预测气动风荷载的主体结构等效静风荷载的抗风设计基本框架,并通过国内某超大跨度干煤棚张弦结构进行了有效性验证。结果表明：采用本文提出的风荷载数据库预测与等效静风荷载方法效率较高,且能够在一定程度包络风振响应分析结果,可用于结构初步设计阶段对主体结构设计风荷载快速预估。     

BP人工神经网络模型在建筑物沉降预测中应用

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。     

Prediction of blast-induced ground vibration using artificial neural network

An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency.     

基于小波变换的神经网络空调负荷预测研究

基于小波变换的思想建立了递归BP网络模型来预测空调负荷,改进了网络权值、闽值的修改算法,引入了折扣系数法以提高近期预测精度,结合一实例进行了空调逐时冷负荷预测,结果表明该方法预测精度高,适用于空调负荷预测。     

基于人工神经网络的混凝土实时强度影响因素敏感性分析

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。     

Prediction of wind speed for the estimation of wind turbine power output from site climatological data using artificial neural network

In this paper, the wind speeds of Noupoort in the Western Cape region of South Africa are forecasted from the site climatological data using feed forward artificial neural network (ANN) with the back propagation training method. Different architectural designs are tested with different combinations of climatological data to obtain the most suitable ANN for predicting the wind speed of the site. The predicted wind speeds are compared with the actual measured wind speeds and the results are evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and correlation coefficient (R). Some of the key results show that combination of temperature, wind direction and time of the day (TEM?+?WD?+?T) could effectively predict wind speed of Noupoort. The forecasted wind speed shows a strong agreement with the measured wind speed with R, RMSE, MAPE and MAE of 0.96, 0.56, 6.64% and 0.44, respectively.     

基于互联网的神经网络空调负荷预测解决方案

在分析比较各种负荷预测方法的基础上，给出了一个基于互联网的应用神经网络方法进行负荷预测的方案。该方法通过互联网以“准在线”的方式可同时满足较高的逐时负荷预测精度和模型调整的要求，并已在实际工程中使用，取得了一定的效果。     

基于模糊神经网络的规则巨型框架结构风荷载特性预测与研究

应用模糊神经网络方法,结合规则的巨型框架结构的风洞试验,成功地预测了表面有凸出梁柱的规则巨型框架结构的风压分布特性.结果表明,采用该方法可以综合考虑各因素的影响,并能有效、简捷地处理常规方法难以解决的问题.     

Prediction of blast-induced ground vibration using artificial neural networks

Blasting is still being considered to be one the most important applicable alternatives for conventional tunneling. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby habitants and dwellings and should be prevented. In this paper, an attempt has been made to predict blast-induced ground vibration using artificial neural network (ANN) in the Siahbisheh project, Iran. To construct the model maximum charge per delay, distance from blasting face to the monitoring point, stemming and hole depth are taken as input parameters, whereas, peak particle velocity (PPV) is considered as an output parameter. A database consisting of 182 datasets was collected at different strategic and vulnerable locations in and around the project. From the prepared database, 162 datasets were used for the training and testing of the network, whereas 20 randomly selected datasets were used for the validation of the ANN model. A four layer feed-forward back-propagation neural network with topology 4-10-5-1 was found to be optimum. To compare performance of the ANN model with empirical predictors as well as regression analysis, the same database was applied. Superiority of the proposed ANN model over empirical predictors and statistical model was examined by calculating coefficient of determination for predicted and measured PPV. Sensitivity analysis was also performed to get the influence of each parameter on PPV. It was found that distance from blasting face is the most effective and stemming is the least effective parameter on the PPV.     

人工神经网络在岩质边坡稳定性预测中的应用

针对岩质边坡稳定性分析中存在的问题，提出了运用人工神经网络（ANN）预测岩质边坡稳定性的新方法，并构造了相应的BP神经网络模型。预测结果表明，该模型具有很高的预测精度，能够满足实际工程需要。