Multiattribute probabilistic neural network for near-surface field engineering application

Unconfined compressive strength (UCS) is an important rock parameter required in the engineering design of structures built on top or within the interior of rock formations. In a site investigation project, UCS is typically obtained discretely (through point-to-point measurement) and interpolated. This method is less than optimal to resolve meter-scale UCS variations of heterogenous rock such as carbonate formations in which property changes occur within data spacing. We investigate the geotechnical application of multiattribute analysis based on near-surface reflection seismic data to probe rock formations for their strength attributes at meter-scale variability. Two Late Jurassic outcrops located in central Saudi Arabia serve as testing sites: the Hanifa Formation in Wadi Birk and the Jubaila Formation in Wadi Laban. The study uses core and 2D seismic profiles acquired in both sites, from which we constrain UCS, acoustic velocity, density, and gamma-ray values. A positive linear correlation between UCS and acoustic impedance along the core indicates that seismic attributes can be utilized as a method to laterally extrapolate the UCS away from the core location. Seismic colored inversion serves as input for neural network multiattribute analysis and is validated with a blind test. Results from data at both outcrop sites indicate a high degree of consistency with an absolute UCS error of approximately 5%. We also demonstrate the applicability of predicted UCS profiles to interpret mechanical stratigraphy and map lateral UCS heterogeneities. These findings provide a less expensive alternative to constrain UCS from limited core data on a field-scale site engineering project.

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Stability Prediction Model of Roadway Surrounding Rock Based on Concept Lattice Reduction and a Symmetric Alpha Stable Distribution Probability Neural Network

Applied Sciences ◽

10.3390/app8112164 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2164 ◽

Cited By ~ 7

Author(s):

Yang Liu ◽

Yicheng Ye ◽

Qihu Wang ◽

Xiaoyun Liu

Keyword(s):

Neural Network ◽

Prediction Model ◽

Stable Distribution ◽

Probabilistic Neural Network ◽

High Reliability ◽

Concept Lattice ◽

Engineering Application ◽

Surrounding Rock ◽

Lattice Reduction ◽

Alpha Stable Distribution

To combat the uncertainty of the multiple factors affecting roadway surrounding rock stability, five initial indexes are selected for reduction according to concept lattice theory: rock quality designation (RQD), uniaxial compressive strength (Rc), the integrity coefficient of rock mass, groundwater seepage, and joint condition. The aim of this study is to compute correlation coefficients among various indexes and verify the effectiveness of lattice reduction. Alpha stable distribution is used to replace the commonly used Gauss distribution in probabilistic neural networks. A prediction model for the stability of roadway surrounding rock is then established based on a concept lattice and improved probabilistic neural network. 100 groups of training sample data are plugged into this model one by one to examine its rationality. The established model is employed for engineering application prediction with ten indiscriminate sample groups from the Jianlinshan mining area of the Daye iron mine, revealing accuracy of up to 90%. This demonstrates that our prediction model based on a concept lattice and improved probabilistic neural network has high reliability and applicability.

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Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

Scientific Research Journal ◽

10.24191/srj.v2i2.9330 ◽

2005 ◽

Vol 2 (2) ◽

pp. 25

Author(s):

Noraliza Hamzah ◽

Wan Nor Ainin Wan Abdullah ◽

Pauziah Mohd Arsad

Keyword(s):

Neural Network ◽

Power Quality ◽

Probabilistic Neural Network ◽

Microsoft Excel ◽

Waveform Data ◽

The Neural Network ◽

Harmonic Voltage ◽

Power Quality Disturbances ◽

Electronic Load ◽

Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code. Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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