| 1. |
- Abbaszadeh Shahri, Abbas, et al.
(författare)
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A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis
- 2024
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Ingår i: Engineering with Computers. - : Springer Nature. - 0177-0667 .- 1435-5663. ; 40:3, s. 1501-1516
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Tidskriftsartikel (refereegranskat)abstract
- There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, and thus interpretable 3D subsurface views from such integrated heterogeneous data requires developing a new methodology for convenient post-modeling analyses. To this end, in the current paper a hybrid ensemble-based automated deep learning approach for 3D modeling of subsurface geological bedrock using multisource data is proposed. The uncertainty then was quantified using a novel ensemble randomly automated deactivating process implanted on the jointed weight database. The applicability of the automated process in capturing the optimum topology is then validated by creating 3D subsurface geo-model using laser-scanned bedrock-level data from Sweden. In comparison with intelligent quantile regression and traditional geostatistical interpolation algorithms, the proposed hybrid approach showed higher accuracy for visualizing and post-analyzing the 3D subsurface model. Due to the use of integrated multi-source data, the approach presented here and the subsequently created 3D model can be a representative reconcile for geoengineering applications.
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| 2. |
- Abbaszadeh Shahri, Abbas
(författare)
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An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data : A Case Study in Southwest of Sweden
- 2016
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Ingår i: Geotechnical and Geological Engineering. - : Springer. - 0960-3182 .- 1573-1529. ; , s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Application of artificial neural networks (ANN) in various aspects of geotechnical engineering problems such as site characterization due to have difficulty to solve or interrupt through conventional approaches has demonstrated some degree of success. In the current paper a developed and optimized five layer feed-forward back-propagation neural network with 4-4-4-3-1 topology, network error of 0.00201 and R2 = 0.941 under the conjugate gradient descent ANN training algorithm was introduce to predict the clay sensitivity parameter in a specified area in southwest of Sweden. The close relation of this parameter to occurred landslides in Sweden was the main reason why this study is focused on. For this purpose, the information of 70 piezocone penetration test (CPTu) points was used to model the variations of clay sensitivity and the influences of direct or indirect related parameters to CPTu has been taken into account and discussed in detail. Applied operation process to find the optimized ANN model using various training algorithms as well as different activation functions was the main advantage of this paper. The performance and feasibility of proposed optimized model has been examined and evaluated using various statistical and analytical criteria as well as regression analyses and then compared to in situ field tests and laboratory investigation results. The sensitivity analysis of this study showed that the depth and pore pressure are the two most and cone tip resistance is the least effective factor on prediction of clay sensitivity.
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| 3. |
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| 4. |
- Abbaszadeh Shahri, Abbas, et al.
(författare)
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CPT-SPT correlations using artificial neural network approach : A Case Study in Sweden
- 2015
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Ingår i: Electronic Journal of Geotechnical Engineering. - : E-Journal of Geotechnical Engineering. - 1089-3032. ; 20:28, s. 13439-13460
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Tidskriftsartikel (refereegranskat)abstract
- The correlation between Standard and Cone Penetration Tests (SPT and CPT) as two of the most used in-situ geotechnical tests is of practical interest in engineering designs. In this paper, new SPT-CPT correlations for southwest of Sweden are proposed and developed using an artificial neural networks (ANNs) approach. The influences of soil type, depth, cone tip resistance, sleeve friction, friction ratio and porewater pressure on obtained correlations has been taken into account in optimized ANN models to represent more comprehensive and accurate correlation functions. Moreover, the effect of particle mean grain size and fine content were investigated and discussed using graph analyses. The validation of ANN based correlations were tested using several statistical criteria and then compared to existing correlations in literature to quantify the uncertainty of the correlations. Using the sensitivity analyses, the most and least effective factors on CPT-SPT predictions were recognized and discussed. The results indicate the ability of ANN as an attractive alternative method regarding to conventional statistical analyses to develop CPT-SPT relations.
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| 5. |
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| 6. |
- Abbaszadeh Shahri, Abbas, et al.
(författare)
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Landslide susceptibility hazard map in southwest Sweden using artificial neural network
- 2019
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Ingår i: Catena (Cremlingen. Print). - : ELSEVIER. - 0341-8162 .- 1872-6887. ; 183
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Tidskriftsartikel (refereegranskat)abstract
- Landslides as major geo-hazards in Sweden adversely impact on nearby environments and socio-economics. In this paper, a landslide susceptibility map using a proposed subdivision approach for a large area in southwest Sweden has been produced. The map has been generated by means of an artificial neural network (ANN) model developed using fourteen causative factors extracted from topographic and geomorphologic, geological, land use, hydrology and hydrogeology characteristics. The landslide inventory map includes 242 events identified from different validated resources and interpreted aerial photographs. The weights of the causative factors employed were analyzed and verified using accepted mathematical criteria, sensitivity analysis, previous studies, and actual landslides. The high accuracy achieved using the ANN model demonstrates a consistent criterion for future landslide susceptibility zonation. Comparisons with earlier susceptibility assessments in the area show the model to be a cost-effective and potentially vital tool for urban planners in developing cities and municipalities.
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| 7. |
- Abbaszadeh Shahri, Abbas, et al.
(författare)
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Modified correlations to predict the shear wave velocity using piezocone penetration test data and geotechnical parameters : a case study in the southwest of Sweden
- 2016
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Ingår i: INNOVATIVE INFRASTRUCTURE SOLUTIONS. - : SPRINGER INTERNATIONAL PUBLISHING AG. - 2364-4176 .- 2364-4184. ; 1:1
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Tidskriftsartikel (refereegranskat)abstract
- Shear wave velocity (VS) is an important geotechnical characteristic for determining dynamic soil properties. When no direct measurements are available, V-S can be estimated based on correlations with common in situ tests, such as the piezocone penetration test (CPTu). In the current paper, three modified equations to predict the V-S of soft clays based on a comprehensive provided CPTu database and related geotechnical parameters for southwest of Sweden were presented. The performance of the obtained relations were examined and investigated by several statistical criteria as well as graph analyses. The best performance was observed by implementing of corrected cone tip resistance (q(t)) and pore pressure ratio (B-q) which directly can be found from CPTu data. The introduced modifications were developed and validated for available soft clays of the studied area in southwest of Sweden, and thus, their applicability for proper prediction in other areas with different characteristics should be controlled. However, the used method as a suitable tool can be employed to investigate.
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| 8. |
- Abbaszadeh Shahri, Abbas, et al.
(författare)
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Normalizing Large Scale Sensor-Based MWD Data : An Automated Method toward A Unified Database
- 2024
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 24:4
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Tidskriftsartikel (refereegranskat)abstract
- In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.
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| 9. |
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| 10. |
- Abbaszadeh Shahri, Abbas, et al.
(författare)
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Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test
- 2016
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Ingår i: INNOVATIVE INFRASTRUCTURE SOLUTIONS. - : SPRINGER INTERNATIONAL PUBLISHING AG. - 2364-4176 .- 2364-4184. ; 1:1
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Tidskriftsartikel (refereegranskat)abstract
- Although there are many proposed relations for different rock types to predict the uniaxial compressive strength (UCS) as a function of P-wave velocity (V-P) and point load index (Is), only a few of them are focused on marlstones. However, these studies have limitations in applicability since they are mainly based on local studies. In this paper, an attempt is therefore made to present updated relations for two previous proposed correlations for marlstones in Iran. The modification process is executed through multivariate regression analysis techniques using a provided comprehensive database for marlstones in Iran, including UCS, V-P and Is from publications and validated relevant sources comprising 119 datasets. The accuracy, appropriateness and applicability of the obtained modifications were tested by means of different statistical criteria and graph analyses. The conducted comparison between updated and previous proposed relations highlighted better applicability in the prediction of UCS using the updated correlations introduced in this study. However, the derived updated predictive models are dependent on rock types and test conditions, as they are in this study.
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