As explained previously, advanced numerical simulations may be used for predicting earthquake hazards and disasters. Large-scale numerical simulations may be carried out due to the developed computer science and technology. In this process, all phases of a possible earthquake should be simulated. Numerical simulation should give researchers further information about estimation of variability in earthquake disasters due to different earthquake scenarios. The amount and complexity of the data to be used for earthquake hazard and disaster simulations of large areas require the use of user-friendly and advanced graphical interface systems. With the advances in computer technologies, the computer-aided design (CAD) has been developed. MATLAB is one of the representatives of high-performance language for the CAD. It can be used for modeling, simulating, and analyzing dynamic systems. It supports linear and nonlinear systems, modeled in continuous and discrete time. Simulation is an interactive process, so the parameters may be altered, while the simulation is running and the system response may be immediately monitored.
A new user-friendly and graphical user interface-based system development for earthquake hazard and disaster simulation of urban areas has been started (Sahin 2014). The system is planned to include all phases of IES system. The final aim of the system is to simulate earthquake strong ground motion from source to site, structural damages, and human behavior during the earthquake. The long-term objective is simulation of emergency evacuation and short- and long-term social and economic recovery. It is possible to automatically construct a computer model for a city of some hundred kilometers scale using the latest geographical information system (GIS). It can be said that new IES system provides vital information to see the possible effects of future earthquake hazards and disasters.
High-performance computing enhancement was made, so that urban area models of higher fidelity were analyzed in shorter times and ensemble simulation which accounted for uncertainties in modeling the earthquake hazard and disaster processes could be made. The uncertainties in modeling the earthquake hazard and disaster processes are material or structural properties. It is not possible to remove these uncertainties, but we can account for it by providing a probabilistic distribution of seismic response carrying out ensemble computing. It should be noted that material property uncertainty is not significant for a structure if input ground motion is small. But the uncertainty provides wider range of responses if input ground motion is large and the structure has a chance of being damaged. Full-scale vibration tests will be carried out to eliminate structural uncertainties, and structural models will be developed.
It should be indicated that high-performance computing can be directly applied to the IES system developed on MATLAB by using Parallel Computing Toolbox. Multicore processors, GPUs, and computer clusters can be used to solve computationally and data-intensive problems. The applications developed in MATLAB can be parallelized without CUDA or MPI programming. High-level constructs parallel for loops, special array types, and parallelized numerical algorithms can be used for parallelization (Parallel Computing Toolbox™ User’s Guide 2015).
In IES system, physical modeling is used and more detailed modeling decreases the uncertainties. The soil models are directly developed from site observations, and seismic behavior of soil models can be observed. The uncertainties in soil models and hazard simulations have been eliminated significantly by integrating all soil test data such as borehole, ReMi, and PS Log. The integration algorithms are used for producing more accurate soil models. The mesh grid intervals could be shortened after considering all data sets with GIS format, and more reliable soil models could be generated.
In building models, it could be possible to predict detailed 3D FEM models from the estimated multidegree-of-freedom (MDOF) models. Detailed time history analysis of thousands of buildings could be possible owing to high-performance computing. As indicated before, more detailed models decrease the uncertainties in modeling.
Automatic soil model generation and earthquake hazard simulation tool—SoVeLAB
The ground motion prediction is the first step of earthquake damage assessment. This analysis generally consists of two processes. In the first process, the ground motion on seismic bedrock is calculated. In the second process, the soil effects are evaluated by considering seismic site amplification (site response analysis). The surface ground motion can be predicted by considering both processes. Wave propagation in seismic bedrock is evaluated in the first process, and wave propagation in soil surface is evaluated in the second process.
The seismic bedrock motion can be considered with three ways: (1) converting observed rock outcrop motion into bedrock motion, (2) directly applying a seismic record or random record as a bedrock motion, and (3) obtaining bedrock motion from the source by applying wave propagation techniques.
The surface ground layer model may be constructed by using a set of geotechnical data, which include boring, PS logging, or passive seismic methods such as microtremor and refraction microtremor (ReMi). An algorithm is used to automatically construct three-dimensional soil model. In the proposed algorithm, interpolation and extrapolation between neighboring ReMi and boreholes are performed.
The collected field data are scattered over the study area, and it is required to get them gridded with respect to generated mesh. This mesh generation is automatically done by developed system. Automatic interpolation is utilized, and soil profile can be obtained at any point on the produced mesh. The mesh generation process is summarized in Fig. 2.
Soil profiles at any point on mesh are generated by determining each layer’s top and bottom elevation. Borehole data are utilized for determining the top and bottom elevations of the layers. Figure 3 shows the procedure used to combine soil profiles at borehole locations. In the developed system, the procedure is conducted in three-dimensional domain. The upper and lower boundaries of the soil layers are combined. If any layer is not encountered in a borehole log, the following soil layer from the stratigraphy is assigned in place of it. These modifications mean that every formation exists in all borehole logs, but in some of them the nonexisting formations have zero thickness.
After that process layer thicknesses at any point in the study can be calculated by only interpolation. The same procedure is applied for generating the velocity model. The soil and velocity models are then integrated. The velocity information at any depth of ReMi test locations is obtained, and the same procedure is used to combine them.
To generate soil model of a selected urban area from borehole and refraction microtremor test data, a new MATLAB code, named as SoVeLAB (Soil Velocity Laboratory), was developed. The flow diagram given in Fig. 4 represents the algorithm of this program. Firstly, SoVeLAB reads the area data which includes the boundary coordinates of the region being studied. At this step, SoVeLAB generates a uniformly gridded mesh between the defined boundary coordinates. Secondly, SoVeLAB reads the borehole and refraction microtremor test data from the GIS database. Then, SoVeLAB visualizes the altitude map of the study region by utilizing the x, y coordinates and altitude of site test location. Interpolating the borehole test data, SoVeLAB produces the primer soil model including layer boundaries, while interpolating the layer depths, SoVeLAB uses a filter mask to filter out unrealistic results. MATLAB Image Processing Toolbox filtering functions are used for median filtering. Median filtering seems more effective than convolution technique if it is aimed to simultaneously reduce noise and preserve edges. After generation of primer soil model, SoVeLAB interpolates refraction microtremor test data and generates the velocity model. Firstly, the study area is divided into small cell and the data sets are combined by linear interpolation. The shear wave velocities at each 1 m depth for each observation stations are determined up to the maximum depth reached. Then, the velocity values over the study area for each 1 m depth are interpolated. This procedure successfully generates the three-dimensional digital velocity model. To ensure that unrealistic data are thrown out, velocity model is also filtered. The system is filtered according to median value of neighboring matrix elements. According to the Vs value representing the engineering bedrock, SoVeLAB determines the bedrock depth and integrates bedrock layer with primer soil model and generates main soil model. Finally, SoVeLAB combines velocity model and soil model to use in site response analyses.
It should be noted that two-dimensional median filtering is used for eliminating unrealistic test data. Median filtering is a nonlinear filtering operation and widely used in image processing. It can be seen that a median filter is very effective in automatic soil model generation with great variety and great number of data.
Automatic city model generation and earthquake disaster simulation tool—CitySeis
City simulation consists of earthquake hazard simulation and earthquake disaster simulation. In this section, the development of earthquake disaster simulation system is explained. A new virtual city simulator called CitySeis has been developed for earthquake disaster simulation of urban areas (Sahin 2014). The MDOF models are automatically generated from the GIS data. The global corner coordinates of the buildings, building type, number of stories, and other useful data can be read from GIS database, and the mass, stiffness, and rigidity matrices are automatically generated. The detailed information for this procedure can be found in Hori (2007).
It should be noted that the GIS data include building type, number of stories, and global coordinates of the buildings. The structural model is generated by using these data. The system does not need material properties, and the stiffness and mass matrices are directly obtained with iteration process.
Any earthquake data can be loaded into the system, and earthquake analysis of the virtual city is carried out. The virtual city is automatically generated from the GIS data, and the building inventory is analyzed under earthquake motion. The behavior of each building in the virtual city may be observed and evaluated.
The advanced graphical and simulation capabilities of MATLAB are used. The computer model of surface ground layers and the computer model of residential building assembly are automatically constructed by making use of data stored in available GIS. In the developed system, an algorithm for constructing a linear MDOF system for a residential building with the aid of available GIS data is used. The input data for this algorithm are the global coordinates of the building, story height, and building type.