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Today, the built environment, including infrastructure for tunnels, bridges, highways, subways, railroads, harbors, buildings and airports, is responsible for a significant portion of the energy consumption, natural resource utilization, waste generation as well as CO2 and other environmentally harmful emissions in the United States and around the world. There is no silver bullet solution to achieve the ambitious goal of zero carbon buildings and a city infrastructure with significantly reduced CO2 emissions. Thus, multifaceted solutions should be developed. Another challenge associated with the built environment is aging and a large economic burden to upgrade and maintain the outdated infrastructure. The current status of the U.S. built environment is far below sustainable condition. Rapidly deteriorating infrastructure that must be replaced provides us with the unique opportunity to rethink where and how we should live in the future. In addition, current challenges related to economic and societal inequality in the United States and other global communities also force us to re-evaluate how humanity is connected and how we share resources for a sustainable and healthy future while keeping the Earth safe. The engineering solutions for our future built environment include, but are not limited to, the design and synthesis of new infrastructure materials with low carbon intensity, the development of new manufacturing options and technologies, and the integration of innovative functionalities into building envelopes. [ABSTRACT FROM AUTHOR]

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 investigation experimentally examines the effect of incorporating alumina nanoparticles on the off-axis stiffness and stress-relaxation of carbon fiber-reinforced polymer composites. Four epoxy–alumina nanoparticle nanocomposites incorporating 0.0, 1.0, 2.0, and 3.0 wt% alumina nanoparticles of the total weight of epoxy are examined. Off-axis tension stiffness and stress-relaxation tests were performed on carbon fiber-reinforced polymer coupons fabricated with alumina nanoparticles–epoxy nanocomposites. Dynamic mechanical analysis testing of neat epoxy and epoxy nanocomposites incorporating alumina nanoparticles was used to identify the stiffness and relaxation behavior of the alumina nanoparticles–epoxy nanocomposite matrix. Fourier transform infrared spectroscopy was used to observe chemical changes when alumina nanoparticles are mixed with epoxy. It is shown that using alumina nanoparticles at a concentration close to 2.0 wt%, can reduce the off-axis stiffness by ∼30% and increase the off-axis stress-relaxation of carbon fiber-reinforced polymer by ∼10%. [ABSTRACT FROM AUTHOR]

Glass fiber reinforced polymer (GFRP) composites are widely used in infrastructure applications including water structures due to their relatively high durability, high strength to weight ratio, and non-corrosiveness. Here we demonstrate the potential use of carbon nanoparticles dispersed during GFRP composite fabrication to reduce water absorption of GFRP and to enable monitoring of moisture damage propagation in GFRP composites. GFRP coupons incorporating 2.0 wt % carbon nanofibers (CNFs) and 2.0 wt % multi-wall carbon nanotubes (MWCNTs) were fabricated in order to study the effect of moisture damage on mechanical properties of GFRP. Water absorption tests were carried out by immersing the GFRP coupons in a seawater bath at two temperatures for a time period of three months. Effects of water immersion on the mechanical properties and glass transition temperature of GFRP were investigated. Furthermore, moisture damage in GFRP was monitored by measuring the electrical conductivity of the GFRP coupons. It was shown that carbon nanoparticles can provide a means of self-sensing that enables the monitoring of moisture damage in GFRP. Despite the success of the proposed technique, it might not be able to efficiently describe moisture damage propagation in GFRP beyond a specific threshold because of the relatively high electrical conductivity of seawater. Microstructural investigations using Fourier Transform Infrared (FTIR) explained the significance of seawater immersion time and temperature on the different levels of moisture damage in GFRP. [ABSTRACT FROM AUTHOR]

In this work, we report the potential use of novel carbon nanofibers (CNFs), dispersed during fabrication of glass fiber composites to monitor damage propagation under static loading. The use of CNFs enables a transformation of the typically non-conductive glass fiber composites into new fiber composites with appreciable electrical conductivity. The percolation limit of CNFs/epoxy nanocomposites was first quantified. The electromechanical responses of glass fiber composites fabricated using CNFs/epoxy nanocomposite were examined under static tension loads. The experimental observations showed a nonlinear change of electrical conductivity of glass fiber composites incorporating CNFs versus the stress level under static load. Microstructural investigations proved the ability of CNFs to alter the polymer matrix and to produce a new polymer nanocomposite with a connected nanofiber network with improved electrical properties and different mechanical properties compared with the neat epoxy. It is concluded that incorporating CNFs during fabrication of glass fiber composites can provide an innovative means of self-sensing that will allow damage propagation to be monitored in glass fiber composites. [ABSTRACT FROM AUTHOR]