Artificial neural network analysis of twin tunnelling-induced ground settlements

In this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.

Artificial ornaments manipulate intrinsic male quality in wild-caught zebra finches (Taeniopygia guttata)

The addition of red and green color bands is a commonly used method for manipulating male attractiveness in the zebra finch (Taeniopygia guttata), providing insight into the study of maternal investment and sexual selection. The addition of artificial ornaments has been assumed to manipulate a females’ perception of the male, rather than affecting intrinsic qualities of the male himself. Here, however, we reveal that the artificial band color worn by a male changes his body mass, condition, and courtship display rate. Males wearing red color bands in aviaries prior to mate-choice trials had a significantly higher song rate during trials than those wearing green color bands, alongside a significant increase in mass change and condition. Male song rate was found to significantly correlate with female preference alongside a female preference for red-banded males. However, male song rate in turn increased when female response was positive, suggesting a social feedback between the interacting birds. Our data suggest the presence of socially mediated feedback mechanisms whereby the artificial increase in attractiveness or dominance of a male directly affects other aspects of his attractiveness. Therefore, housing birds in social groups while manipulating attractiveness can directly influence other male qualities and should be accounted for by future studies.

Artificial Muscles Based on Polypyrrole/Carbon Nanotube Laminates

A novel method for the periodical assembly of laminates of forest-drawn carbon nanotube (CNT) sheets and polypyrrole (PPy) is described. The method produces composite films in which the volume fraction and orientation of CNTs can be controlled. Actuator stroke and strength is increased and work capacity per cycle doubled when nanotube orientation is perpendicular to the actuation direction. Most importantly, these PPy/CNT laminates have dramatically decreased creep during actuation, which has been a major barrier for the application of PPy actuators.

Predicting house damage class using artificial intelligence method

The unsatisfactory performance of light structures founded on expansive soils subject to seasonal movements is frequently reported since the early 1950's in Australia. Excessive movements have caused damage to numerous structures that have not been adequately designed to accommodate soil volume changes. However, the sole presence of expansive soil is not necessarily the main cause of damage. Other factors such as vegetation, climate factors, types of construction materials and geology type may also contribute. This paper presents a model which predicts the damage class by analyzing combinations of the contributing factors using artificial intelligence methods. This model can help to identify if any serious and urgent repairs are necessary and immediate actions could be initiated without delay.

Use of artificial neural networks as explicit finite difference operators

Results of a numerical exercise, substituting a numerical operator by an artificial neural network (ANN) are presented in this paper. The numerical operator used is the explicit form of the finite difference (FD) scheme. The FD scheme was used to discretize the one-dimensional transport equation, which included both the advection and dispersion terms. Inputs to the ANN are the FD representation of the transport equation, and the concentration was designated as the output. Concentration values used for training the ANN were obtained from analytical solutions. The numerical operator was reconstructed from a back calculation of the weights of the ANN. Linear transfer functions were used for this purpose. The ANN was able to accurately recover the velocity used in the training data, but not the dispersion coefficient. This capability was improved when numerical dispersion was taken into account; however, it is limited to the condition: C/P<0.5 , where C is the Courant number and P , the Peclet number (i.e., the restriction imposed by the Neumann stability condition).

Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN

DOC and UVA attenuation with soil aquifer treatment in the saturated zone of an artificial coastal sandfill

Results from a direct recharge experiment conducted in the field to investigate DOC and UVA(254) attenuation rates during the direct injection of UF treated wastewater into a artificial coastal sandfill are presented in this paper. Approximately 500 m(3) of ultra-filtered wastewater was injected into the saturated zone, over a period of 9 days. The movement of the plume was tracked over 80 days, during which time samples were obtained from multilevel samplers installed in transects across the drift axis of the plume. An analysis of fluorescein in the samples obtained during the drift of the UF plume showed that DOC and UVA were attenuated beyond rates predicted by conservative mixing, by up to a maximum of 45%. A degradation coefficient of 0.0175 day(-1) was found to be applicable for DOC degradation. After a drift period of 80 days, DOC and UVA reduced to approximately 4.5 mg/l and 0.100 cm(-1), respectively, from initial values of 8.06 mg/l and 0.199 cm(-1).