Search

This paper describes an alternative perspective in evaluating of computational models for predicting deflection of cracked reinforced concrete (RC) structures. The concept of robustness to uncertainty in modeling input parameters is introduced in addition to the classical perspective of modeling accuracy. A new method to quantify robustness to uncertainty is proposed and applied to the problem of modeling deflection of RC structures. Monte Carlo simulation is used for uncertainty propagation in serviceability calculations. The propagation of random uncertainties in deflection of a continuous one-way RC slab is examined. We introduce a method for the practical use of the proposed robustness metric by considering the reliability analysis. A cracked plane frame model to simulate deflection of the RC slab was adopted. We show that by considering a threshold of uncertainty in predicted deflection, the robustness to uncertainty in model inputs for different deflection prediction models can be evaluated. Our investigation proves that a tradeoff between model robustness and model accuracy exists in modeling deflection of RC structures. DOI: 10.1061/(ASCE)0733-9445(2009)135:8(998) CE Database subject headings: Concrete structures: Deflection; Uncertainty principles; Serviceability; Reinforced concrete.

A genetic-fuzzy learning from examples (GFLFE) approach is presented for determining fuzzy rule bases generated from input/output data sets. The method is less computationally intensive than existing fuzzy rule base learning algorithms as the optimization variables are limited to the membership function widths of a single rule, which is equal to the number of input variables to the fuzzy rule base. This is accomplished by primary width optimization of a fuzzy learning from examples algorithm. The approach is demonstrated by a case study in masonry bond strength prediction. This example is appropriate as theoretical models to predict masonry bond strength are not available. The GFLFE method is compared to a similar learning method using constrained nonlinear optimization. The writers' results indicate that the use of a genetic optimization strategy as opposed to constrained nonlinear optimization provides significant improvement in the fuzzy rule base as indicated by a reduced fitness (objective) function and reduced root-mean-squared error of an evaluation data set. DOI: 10.1061/(ASCE)0887-3801(2009)23:3(193) CE Database subject headings: Fuzzy sets; Bonding; Artificial intelligence; Optimization; Predictions.

This study investigates the effect of alumina nanoparticles (ANPs) on tension and fracture characteristics of polymer concrete (PC). ANPs with a maximum particle size of 50 nm were used at 0.5, 1.0, 2.0, and 3.0 wt.% of epoxy resin. Tensile strength, tensile failure strain, and fracture toughness (KIC, GIC, and JIC) were determined experimentally. A PC with superior ductility showing a tensile failure strain of 4.89% (compared with 2.56% for neat PC) was observed at ANP content of 3.0 wt.%. Using ANPs in producing epoxy PC can significantly improve ductility (þ60.6%) and fracture toughness (þ131.8%) compared with neat PC. Scanning electron microscope (SEM), dynamic mechanical analyzer (DMA), and Fourier transform infrared (FTIR) observations were conducted to understand the role ANPs play to manifest the observed improvements in tension and fracture characteristics of PC. [ABSTRACT FROM AUTHOR]