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Abbaszadeh Shahri, Abbas
Publications (6 of 6) Show all publications
Ghaderi, A., Abbaszadeh Shahri, A. & Larsson, S. (2019). An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu). Bulletin of Engineering Geology and the Environment, 78(6), 4579-4588
Open this publication in new window or tab >>An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)
2019 (English)In: Bulletin of Engineering Geology and the Environment, ISSN 1435-9529, E-ISSN 1435-9537, Vol. 78, no 6, p. 4579-4588Article in journal (Refereed) Published
Abstract [en]

Soil types mapping and the spatial variation of soil classes are essential concerns in both geotechnical and geoenvironmental engineering. Because conventional soil mapping systems are time-consuming and costly, alternative quick and cheap but accurate methods need to be developed. In this paper, a new optimized multi-output generalized feed forward neural network (GFNN) structure using 58 piezocone penetration test points (CPTu) for producing a digital soil types map in the southwest of Sweden is developed. The introduced GFNN architecture is supported by a generalized shunting neuron (GSN) model computing unit to increase the capability of nonlinear boundaries of classified patterns. The comparison conducted between known soil type classification charts, CPTu interpreting procedures, and the outcomes of the GFNN model indicates acceptable accuracy in estimating complex soil types. The results show that the predictability of the GFNN system offers a valuable tool for the purpose of soil type pattern classifications and providing soil profiles.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG, 2019
Keywords
Soil type mapping, Cone penetration test, Artificial neural network
National Category
Geotechnical Engineering
Identifiers
urn:nbn:se:kth:diva-259422 (URN)10.1007/s10064-018-1400-9 (DOI)000482240400049 ()2-s2.0-85055539172 (Scopus ID)
Note

QC 20190924

Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-09-24Bibliographically approved
Abbaszadeh Shahri, A., Spross, J., Johansson, F. & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena (Cremlingen. Print), 183, Article ID UNSP 104225.
Open this publication in new window or tab >>Landslide susceptibility hazard map in southwest Sweden using artificial neural network
2019 (English)In: Catena (Cremlingen. Print), ISSN 0341-8162, E-ISSN 1872-6887, Vol. 183, article id UNSP 104225Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
ELSEVIER, 2019
Keywords
Landslide, GIS, Sweden, Artificial neural network
National Category
Earth and Related Environmental Sciences
Research subject
Civil and Architectural Engineering, Soil and Rock Mechanics
Identifiers
urn:nbn:se:kth:diva-262756 (URN)10.1016/j.catena.2019.104225 (DOI)000488417700047 ()2-s2.0-85071591343 (Scopus ID)
Note

QC 20191023

Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2019-12-03Bibliographically approved
Abbaszadeh Shahri, A., Spross, J., Johansson, F. & Larsson, S. (2018). Kartering av skredbenägenhet medartificiell intelligens. Bygg & teknik (1)
Open this publication in new window or tab >>Kartering av skredbenägenhet medartificiell intelligens
2018 (Swedish)In: Bygg & teknik, ISSN 0281-658X, no 1Article in journal (Other academic) Published
Place, publisher, year, edition, pages
Förlags AB Bygg & teknik, 2018
National Category
Geotechnical Engineering
Identifiers
urn:nbn:se:kth:diva-238799 (URN)
Note

QC 20181214

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-12-14Bibliographically approved
Abbaszadeh Shahri, A., Spross, J., Johansson, F. & Larsson, S. (2018). Storskalig kartering av skredbenägenhet i västra Götaland med artificiell intelligens. In: : . Paper presented at Grundläggningsdagen 2018 (pp. 107-113). SGF - Svenska geotekniska föreningen
Open this publication in new window or tab >>Storskalig kartering av skredbenägenhet i västra Götaland med artificiell intelligens
2018 (Swedish)Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
SGF - Svenska geotekniska föreningen, 2018
National Category
Geotechnical Engineering
Identifiers
urn:nbn:se:kth:diva-238850 (URN)
Conference
Grundläggningsdagen 2018
Note

QC 20181214

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-12-14Bibliographically approved
Abbaszadeh Shahri, A. & Naderi, S. (2016). Modified correlations to predict the shear wave velocity using piezocone penetration test data and geotechnical parameters: a case study in the southwest of Sweden. INNOVATIVE INFRASTRUCTURE SOLUTIONS, 1(1), Article ID UNSP 13.
Open this publication in new window or tab >>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 (English)In: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, no 1, article id UNSP 13Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG, 2016
Keywords
Shear wave velocity, Piezocone penetration test, Modified equation, Soft clays, Geotechnical parameters
National Category
Geotechnical Engineering
Identifiers
urn:nbn:se:kth:diva-214915 (URN)10.1007/s41062-016-0014-y (DOI)000409244300013 ()
Note

QC 2017-09-26

Available from: 2017-09-26 Created: 2017-09-26 Last updated: 2017-09-26Bibliographically approved
Abbaszadeh Shahri, A., Larsson, S. & Johansson, F. (2016). Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test. INNOVATIVE INFRASTRUCTURE SOLUTIONS, 1(1), Article ID UNSP 17.
Open this publication in new window or tab >>Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test
2016 (English)In: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, no 1, article id UNSP 17Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG, 2016
Keywords
Updated models, Model performance, Marlstone, Prediction
National Category
Applied Mechanics Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-214914 (URN)10.1007/s41062-016-0016-9 (DOI)000409244300017 ()
Note

QC 2017-09-26

Available from: 2017-09-26 Created: 2017-09-26 Last updated: 2017-09-26Bibliographically approved
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