Effects of unconfined concrete strength on FRP confinement of concrete

Research has shown that fibre reinforced polymer (FRP) wraps are effective for strengthening concrete columns for increased axial and flexural load and deformation capacity, and this technique is now used around the world. The experimental study presented in this paper is focused on the mechanics of FRP confined concrete, with a particular emphasis on the influence of the unconfined concrete compressive strength on confinement effectiveness and hoop strain efficiency. An experimental programme was undertaken to study the compressive strength and stress-strain behaviour of unconfined and FRP confined concrete cylinders of different concrete strength but otherwise similar mix designs, aggregates, and constituents. This was accomplished by varying only the water-to-cement ratio during concrete mixing operations. Through the use of high-resolution digital image correlation to measure both axial and hoop strains, the observations yield insights into the mechanics of FRP confinement of concretes of similar composition but with varying unconfined concrete compressive strength.

US Patent: Method of manufacture of a composite concrete article

A method of manufacturing a composite concrete article comprising forming a textile structure, removing material from regions of the textile structure to create voids in the textile structure and incorporating the textile structure into a body of wet uncured concrete such that the concrete flows into the voids created in the textile structure, embedding the textile structure into the concrete, whereby the textile structure defines at least a portion of a surface of the cured concrete article.

Alkali activated fuel ash and slag mixes:optimization study from paste to concrete building blocks

Alkali activated binders, based on ash and slag, also known as geopolymers, can play a key role in reducing the carbon footprint of the construction sector by replacing ordinary Portland cement in some concretes. Since 1970s, research effort has been ongoing in many research institutions. In this study, pulverized fuel ash (pfa) from a UK power plant, ground granulated blast furnace slag (ggbs) and combinations of the two have been investigated as geopolymer binders for concrete applications. Activators used were sodium hydroxide and sodium silicate solutions. Mortars with sand/binder ratio of 2.75 with several pfa and ggbs combinations have been mixed and tested. The optimization of alkali dosage (defined as the Na2O/binder mass ratio) and modulus (defined as the Na2O/SiO2 mass ratio) resulted in strengths in excess of 70 MPa for tested mortars. Setting time and workability have been considered for the identification of the best combination of pfa/ggbs and alkali activator dosage for different precast concrete products. Geopolymer concrete building blocks have been replicated in laboratory and a real scale factory trial has been successfully carried out. Ongoing microstructural characterization is aiming to identify reaction products arising from pfa/ggbs combinations.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.