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  • 1.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test2016In: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, no 1, article id UNSP 17Article in journal (Refereed)
    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.

  • 2.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Naderi, Shima
    Modified correlations to predict the shear wave velocity using piezocone penetration test data and geotechnical parameters: a case study in the southwest of Sweden2016In: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, no 1, article id UNSP 13Article in journal (Refereed)
    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.

  • 3.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Spross, Johan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Kartering av skredbenägenhet medartificiell intelligens2018In: Bygg & teknik, ISSN 0281-658X, no 1Article in journal (Other academic)
  • 4.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Spross, Johan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Landslide susceptibility hazard map in southwest Sweden using artificial neural network2019In: Catena (Cremlingen. Print), ISSN 0341-8162, E-ISSN 1872-6887, Vol. 183, article id UNSP 104225Article in journal (Refereed)
    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.

  • 5.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Spross, Johan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Storskalig kartering av skredbenägenhet i västra Götaland med artificiell intelligens2018Conference paper (Other academic)
  • 6.
    Ghaderi, Abdolvahed
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Abbaszadeh Shahri, Abbas
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics. Islamic Azad Univ, Roudehen Branch, Fac Civil Engn, Tehran, Iran..
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)2019In: Bulletin of Engineering Geology and the Environment, ISSN 1435-9529, E-ISSN 1435-9537, Vol. 78, no 6, p. 4579-4588Article in journal (Refereed)
    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.

1 - 6 of 6
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