A neurofuzzy system to analyze liquefaction-induced lateral spread

Lateral spreads of liquefied granular soil masses have caused severe damages to many engineered structures. Accordingly, many empirical procedures have been developed from field-direct observations and from multiple regression analyses carried out on the database gathered from many case histories. The intricacy and nonlinearity of the underlying phenomena makes the above approaches somewhat unreliable for estimating liquefaction-induced lateral spreads. The database has inconsistencies and contradictions because of inevitable subjective interpretations and neural network approaches have been proposed for dealing with these. To overcome these difficulties in this paper a hybrid system named neurofuzzy, which profits from fuzzy and neural paradigms, is advanced. The resulting model called NEFLAS (NEuroFuzzy estimation of liquefaction induced LAteral Spread) is shown to yield a much improved forecasting than both multiple regression and neural network procedures. The corresponding software can be obtained from the first author. (c) 2007 Elsevier Ltd. All rights reserved.