Abstract:Abstract. In earthquake-prone areas, site seismic response due to lithostratigraphic sequence plays a key role in seismic hazard assessment. A hybrid model, consisting of GIS and metamodel (model of model) procedures, was introduced aimed at estimating the 1-D spatial seismic site response in accordance with spatial variability of sediment parameters. Inputs and outputs are provided and processed by means of an appropriate GIS model, named GIS Cubic Model (GCM). This consists of a block-layered parametric stru… Show more
“…The potential criteria influencing local seismic amplification susceptibility were determined through a critical review of literature. By reviewing documents on earthquake engineering, seismology, geology, tectonic and structural engineering, geomorphology, and seismic microzonation reports and guidelines (Fäh et al, 1997;Ding et al, 2004;Molina et al, 2010;Mundepi et al, 2010;Marulanda et al, 2012;Hassanzadeh et al, 2013;FEMA, 2014;Fraume et al, 2014;Grelle et al, 2014Grelle et al, , 2016SM Working Group, 2015;Rehman et al, 2016;Nwe and Tun, 2016;GEM, 2017;CAPRA, 2017;Michel et al, 2017;Trifunac, 2016;Hassanzadeh and Nedovic-Budic, 2016), in total 14 influencing criteria were identified (Table 1).…”
Section: Determining the Relevant Criteria By Reviewing Literaturementioning
confidence: 99%
“…Furthermore, Bouckovalas and Papadimitriou (2005) investigated the influence of slope topography in amplifying the peak horizontal seismic ground acceleration suggesting high amplifications near the crest. Grelle et al (2016) presented formulae for topographic amplification on slope surface. These studies indicated that with the increase in slope angle the amplification factor would increase.…”
Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning
confidence: 99%
“…However, this seems simplistic, as it does not consider the elevation differences. Furthermore, Grelle et al (2016) presented an equation that considered the local slope height, relief height, regional share wave velocity, and relief ratio. In addition, several calibration constants should be calculated using 2-D numerical analysis for each study area to compute topographic effects on local seismic amplification.…”
Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning
confidence: 99%
“…Few researchers have considered direct properties of influencing factors in assessing ground-shaking amplification. Even in evaluating developed seismic response models such as SiSeRHMap v1.0 (Grelle et al, 2016) and the GIS cubic model (Grelle et al, 2014), the researchers have applied only lithodynamic, stratigraphic, and topographic effects as influencing factors. Furthermore, Aucelli et al (2018) suggested a method for producing susceptibility index to local seismic amplification in Isernia Province, Italy, and they have considered geological and geomorphological properties of studied areas.…”
Abstract. This paper proposes a new model in evaluating local seismic amplification
susceptibility by considering direct characteristics of influencing criteria
and it deals with uncertainty of modelling through production of fuzzy
membership functions for each criterion. For this purpose, relevant criteria
were identified by reviewing previous literature. These criteria include
alluvial thickness, stiffness and strength of alluvial deposits, type of
soil and particle size distribution of alluvial deposits, depth of
groundwater, type of rock, topographic irregularities, slope, and type of
bedrock. Two methods, analytic hierarchy process (AHP) and fuzzy logic (FL), were applied in order to define priority rank of each criterion and sub-criteria of each criterion through interview data of 10 experts. The criteria and sub-criteria were combined using the weighted linear combination method in GIS to develop a model for assessing local seismic amplification susceptibility in the study area of Bam City, Iran. The model's output demonstrated high to very high seismic amplification levels in central, eastern, northeastern, and northern parts of the study area. The validation results based on overall accuracy and kappa statistics showed 73.6 % accuracy, with 0.74 kappa indicating a good fit to the model's output. This model assists planners and decision makers in determining local seismic amplification susceptibility to be incorporated in designing new development plans of urban and rural areas and in making informed decisions regarding safety measures of existing buildings and infrastructures.
“…The potential criteria influencing local seismic amplification susceptibility were determined through a critical review of literature. By reviewing documents on earthquake engineering, seismology, geology, tectonic and structural engineering, geomorphology, and seismic microzonation reports and guidelines (Fäh et al, 1997;Ding et al, 2004;Molina et al, 2010;Mundepi et al, 2010;Marulanda et al, 2012;Hassanzadeh et al, 2013;FEMA, 2014;Fraume et al, 2014;Grelle et al, 2014Grelle et al, , 2016SM Working Group, 2015;Rehman et al, 2016;Nwe and Tun, 2016;GEM, 2017;CAPRA, 2017;Michel et al, 2017;Trifunac, 2016;Hassanzadeh and Nedovic-Budic, 2016), in total 14 influencing criteria were identified (Table 1).…”
Section: Determining the Relevant Criteria By Reviewing Literaturementioning
confidence: 99%
“…Furthermore, Bouckovalas and Papadimitriou (2005) investigated the influence of slope topography in amplifying the peak horizontal seismic ground acceleration suggesting high amplifications near the crest. Grelle et al (2016) presented formulae for topographic amplification on slope surface. These studies indicated that with the increase in slope angle the amplification factor would increase.…”
Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning
confidence: 99%
“…However, this seems simplistic, as it does not consider the elevation differences. Furthermore, Grelle et al (2016) presented an equation that considered the local slope height, relief height, regional share wave velocity, and relief ratio. In addition, several calibration constants should be calculated using 2-D numerical analysis for each study area to compute topographic effects on local seismic amplification.…”
Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning
confidence: 99%
“…Few researchers have considered direct properties of influencing factors in assessing ground-shaking amplification. Even in evaluating developed seismic response models such as SiSeRHMap v1.0 (Grelle et al, 2016) and the GIS cubic model (Grelle et al, 2014), the researchers have applied only lithodynamic, stratigraphic, and topographic effects as influencing factors. Furthermore, Aucelli et al (2018) suggested a method for producing susceptibility index to local seismic amplification in Isernia Province, Italy, and they have considered geological and geomorphological properties of studied areas.…”
Abstract. This paper proposes a new model in evaluating local seismic amplification
susceptibility by considering direct characteristics of influencing criteria
and it deals with uncertainty of modelling through production of fuzzy
membership functions for each criterion. For this purpose, relevant criteria
were identified by reviewing previous literature. These criteria include
alluvial thickness, stiffness and strength of alluvial deposits, type of
soil and particle size distribution of alluvial deposits, depth of
groundwater, type of rock, topographic irregularities, slope, and type of
bedrock. Two methods, analytic hierarchy process (AHP) and fuzzy logic (FL), were applied in order to define priority rank of each criterion and sub-criteria of each criterion through interview data of 10 experts. The criteria and sub-criteria were combined using the weighted linear combination method in GIS to develop a model for assessing local seismic amplification susceptibility in the study area of Bam City, Iran. The model's output demonstrated high to very high seismic amplification levels in central, eastern, northeastern, and northern parts of the study area. The validation results based on overall accuracy and kappa statistics showed 73.6 % accuracy, with 0.74 kappa indicating a good fit to the model's output. This model assists planners and decision makers in determining local seismic amplification susceptibility to be incorporated in designing new development plans of urban and rural areas and in making informed decisions regarding safety measures of existing buildings and infrastructures.
“…Among the above-mentioned GIS-based models, Grelle et al (2014) have recently introduced a hybrid model, based on the "GIS cubic model (GCM)" frame which is, in turn, based on the concept of lithodynamic units and zones. Here, a lithodynamic unit is defined as a lithological unit, which is characterized by a shear wave velocity-depth-dependent curve (as shown in Fig.…”
Abstract. The SiSeRHMap (simulator for mapped seismic response using a hybrid model) is a computerized methodology capable of elaborating prediction maps of seismic response in terms of acceleration spectra. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches and models are organized in a code architecture composed of five interdependent modules. A GIS (geographic information system) cubic model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A meta-modelling process confers a hybrid nature to the methodology. In this process, the one-dimensional (1-D) linear equivalent analysis produces acceleration response spectra for a specified number of site profiles using one or more input motions. The shear wave velocity–thickness profiles, defined as trainers, are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Emul-spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated evolutionary algorithm (EA) and the Levenberg–Marquardt algorithm (LMA) as the final optimizer. In the final step, the GCM maps executor module produces a serial map set of a stratigraphic seismic response at different periods, grid solving the calibrated Emul-spectra model. In addition, the spectra topographic amplification is also computed by means of a 3-D validated numerical prediction model. This model is built to match the results of the numerical simulations related to isolate reliefs using GIS morphometric data. In this way, different sets of seismic response maps are developed on which maps of design acceleration response spectra are also defined by means of an enveloping technique.
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