Although concrete is a noncombustible material, high temperatures such as those experienced during a fire have a negative effect on the mechanical properties. This paper studies the effect of elevated temperatures on the mechanical properties of limestone, quartzite and granite concrete. Samples from three different concrete mixes with limestone, quartzite and granite coarse aggregates were prepared. The test samples were subjected to temperatures ranging from 25 to 650°C for a duration of 2 h. Mechanical properties of concrete including the compressive and tensile strength, modulus of elasticity, and ultimate strain in compression were obtained. Effects of temperature on resistance to degradation, thermal expansion and phase compositions of the aggregates were investigated. The results indicated that the mechanical properties of concrete are largely affected from elevated temperatures and the type of coarse aggregate used. The compressive and split tensile strength, and modulus of elasticity decreased with increasing temperature, while the ultimate strain in compression increased. Concrete made of granite coarse aggregate showed higher mechanical properties at all temperatures, followed by quartzite and limestone concretes. In addition to decomposition of cement paste, the imparity in thermal expansion behavior between cement paste and aggregates, and degradation and phase decomposition (and/or transition) of aggregates under high temperature were considered as main factors impacting the mechanical properties of concrete. The novelty of this research stems from the fact that three different aggregate types are comparatively evaluated, mechanisms are systemically analyzed, and empirical relationships are established to predict the residual compressive and tensile strength, elastic modulus, and ultimate compressive strain for concretes subjected to high temperatures.

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Prediction of bridge pier scour depth is essential for safe and economical bridge design. Keeping in mind the complex nature of bridge scour phenomenon, there is a need to properly address the methods and techniques used to predict bridge pier scour. Up to the present, extensive research has been carried out for pier scour depth prediction. Different modeling techniques have been applied to achieve better prediction. This paper presents a new soft computing technique called geneexpression programming (GEP) for pier scour depth prediction using laboratory data. A functional relationship has been established using GEP and its performance is compared with other artificial intelligence (AI)-based techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Laboratory data containing 529 datasets was divided into calibration and validation sets. The performance of GEP was found to be highly satisfactory and encouraging when compared to regression equations but was slightly inferior to ANN. This slightly inferior performance of GEP compared to ANN is offset by its capability to provide compact and explicit mathematical expression for bridge scour. This advantage of GEP over ANN is the main motivation for this work. The resulting GEP models will add to the existing literature of AI-based inductive models for bridge scour modeling.

Fabric pilling and handle are important characteristics for home textiles. Treatments, such as preparatory processes and finishing operations, can have a significant effect on the final properties of the fabric. In this research, the pilling performance of bamboo rayon and 50/50 bamboo rayon/cotton fabrics were investigated. Plain and satin weaves were selected. Fabrics were pre-treated with and without singeing. The pilling resistance of singed bamboo rayon and singed bamboo rayon/cotton fabrics were comparatively better than unsigned fabrics. Sanforizing, softening, binder application, and biopolishing were also studied. The binder application technique improved overall pilling performance for plain and satin weave structures without sacrificing fabric characteristics and properties.

This paper describes a simple mathematical technique that uses a genetic algorithm and least squares optimization to obtain a functional approximation (or computer program) for a given data set. Such an optimal functional form is derived from a pre-defined general functional formulation by selecting optimal coefficients, decision variable functions, and mathematical operators. In the past, functional approximations have routinely been obtained through the use of linear and nonlinear regression analysis. More recent methods include the use of genetic algorithms and genetic programming. An example application based on a data set extracted from the commonly used Moody diagram has been used to demonstrate the utility of the proposed method. The purpose of the application was to determine an explicit expression for friction factor and to compare its performance to other available techniques. The example application results in the development of closed form expressions that can be used for evaluating the friction factor for turbulent pipe flow. These expressions compete well in accuracy with other known methods, validating the promise of the proposed method in identifying useful functions for physical processes in a very effective manner. The proposed method is simple to implement and has the ability to generate simple and compact explicit expressions for a given response function.