P. Kamatchi

Neural network-based methodology for inter-arrival times of earthquakes

In this paper, an artificial neural network (ANN)–based methodology is proposed to determine the probability of inter-arrival time (IAT) of main shock of six broad seismic regions of India. Initially, classical methodology using exponential distribution is applied to IAT of earthquake events computed from earthquake catalog data. From the goodness-of-fit test results, it has been found that exponential distribution is not adequate. In this paper, a more efficient ANN-based methodology is proposed, and two ANN models are developed to determine the probability of IAT of earthquake events for a specified region, specified magnitude range or magnitude greater than the specified value. The performance of ANN models developed is validated with number of examples and found to predict the probability with minimal error compared to exponential distribution model. The methodology developed can be applied to any other region with the database of the respective regions.

Long-term prestress loss and camber of box-girder bridge

The evaluation of long-term prestress losses and camber, taking into account the effect of creep, shrinkage, and relaxation, is essential for prestressed concrete bridges. An effort has been made in this paper to identify a suitable time-dependent creep coefficient and shrinkage strain models to estimate the long-term prestress losses and camber for an existing box-girder bridge span. Longterm prestress losses and camber are estimated using four different models for creep and shrinkage-ACI 209R-92, CEB MC90-99, GL2000, and B3-And the comparison has been made with field measurements. From the limited studies made, it is seen that for the initial 5 years after the construction of a bridge, the B3 model can be used for estimation of long-term losses, and the CEB MC90-99 model can be used for estimation of long-term camber. However, for a prestressed concrete beam, the ACI 209R-92 model is found to predict closer prestress loss values with laboratory measurements.

ANN-based Methodology to Determine Dynamic to Static Eccentricity Ratio of Torsionally Coupled Buildings for Site-Specific Earthquakes

Although excessive damage of torsionally coupled buildings situated on soil sites has been reported during past earthquakes, no site-specific study is available having depth of soil stratum above rock as a parameter. In this article, an Artificial Neural Network-based methodology to determine dynamic to static eccentricity ratio of torsionally coupled buildings is proposed. Twelve neural networks are developed using 648,000 patterns from the analyses of shear type single-story building models. The methodology is found to be appropriate to determine dynamic to static eccentricity ratio for multi-story uniform, non uniform framed, and shear wall buildings for site-specific earthquake.

Site-Specific Seismic Analyses Procedures for Framed Buildings for Scenario Earthquakes Including the Effect of Depth of Soil Stratum

The importance of the effect of sediments above bedrock in modifying the strong ground motion has been long recognized (Boore, 2004; Boore & Joyner, 1997; Idriss & Seed, 1970; Seed & Idriss, 1969; Lam et al., 2001; Govindarajulu et al., 2004; Tezcan et al., 2002; Bakir et al., 2005; Kamatchi et al., 2007) in literature. The nature of soil that changes the amplitude and frequency content has a major influence on damaging effects of earthquake. To account for these effects, most of the seismic codes, for example the Indian code (IS 1893 (Part 1) 2002) has defined response spectra for three types of soil viz., hard soil, medium soil and soft soil. As an improvement over this approach, amplification factors based on empirical and theoretical data (Borcherdt, 1994) have been introduced in International Building Codes (IBC, 2009; ASCE 7 2005) for site classes A to E for the short period range and long period range based on the average shear wave velocity of top 30 m soil stratum. For site class F (soft soil) it has been recommended that site-specific analysis need to be carried out. However, Sun et al. (2005) showed that the site coefficients specified in IBC 2000 (IBC 2000) are not valid for Korean Peninsula due to the large difference in the depth of bedrock and the soil stiffness profile. Further, building codes are highly simplified tools and do not adequately represent any single earthquake event from a probable source for the site under consideration. Recently, it is being suggested (Heuze et al., 2004 ) that in addition to use of seismic code provisions, site-specific analysis which includes generation of strong ground motion at bedrock level and propagating it through soil layers (Heuze et al., 2004; Mammo 2005; Balendra et al., 2002) and arriving at the design ground motions and response spectra at surface should also be carried out. In this chapter the procedure to carry out site-specific seismic analysis of framed buildings is illustrated with examples for Delhi city. Rock outcrop motions are generated for the scenario earthquakes of magnitude, Mw = 7.5, Mw = 8.0 and Mw = 8.5. Three actual soil sites of Delhi have been modeled and the free field surface motions and the response spectra are obtained. It has been observed that the PGA amplifications and the response spectra of the three sites are quite different for the earthquakes considered. The same has reflected in considerable