This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)- connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data from the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the bond reduction coefficient u is then
reported. The contributions of this paper is two folds; (1) it contributes towards a new monotonicity-preserving
data-driven FIS model in fuzzy modeling and (2) it provides a novel solution for estimating the u even without a
complete physical knowledge of unbonded tendons
