Mechanical and acoustic absorption properties of lightweight fly ash/slag-based geopolymer concrete with various aggregates

Acoustic absorption and thermal insulation play a key role in modern buildings to make living comfortable and energy-saving. This paper aims to study the workability, physical and mechanical properties, thermal conductivity, and acoustic absorption of modified geopolymer concrete (GPC) with various types of lightweight aggregates (LWA) such as extruded polystyrene foam beads waste (EPS), vermiculite, or lightweight expanded clay aggregate (LECA). The mixtures of geopolymer concrete have been modified by substituting for the ordinary aggregates (dolomite) by volume with various ratios of 0, 25, 50, 75, and 100% for each type of LWA. Besides, the mechanisms of specimens were examined by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and mapping. The results illustrated that the compressive strength values range between 8.5 and 47.50 MPa. The hardened density of concrete was between 1500 and 2450 kg/m³, and thermal conductivity was between 0.45 and 1.16 W/m.K. Geopolymer concrete was considered an acoustic absorption and thermally insulating material. Geopolymer concrete was considered an acoustic absorption and thermally insulating material. EPS, vermiculite, and LECA will be beneficial for applications in lightweight geopolymer concrete due to their capability to reduce weight and excellent thermal conductivity, and the property of improving acoustic absorption. The mechanical results indicated that 25% LECA was the best compared with the ratios of other LWA and gained 35.0, 2.7, and 4.3 MPa of compressive, splitting tensile and flexural strength, respectively. It had positive workability; the thermal conductivity was 1.1 W/m.K, and hardened density was decreased to 10% compared to the control. In addition, LECA is considered the superior and suitable material for acoustic absorption compared with other aggregates.     

Carbonation resistance of one industrial mortar used as a concrete coating

In aggressive environments, concrete itself may not be enough to protect the reinforcement against carbon dioxide penetration. This gas reacts with the portlandite of the concrete to form calcium carbonate. This process leads to a pH reduction and, therefore, promotes the depassivation of the steel reinforcement in reinforced concretes. Therefore, a supplementary protection method such as coating with a mortar, as carbonation barrier can be used to provide adequate durability. Experimental data are presented to illustrate the effect of three different testing levels of CO₂ on an industrial mortar applied to a concrete base in order to evaluate its performance as an anti-carbonation barrier. The results from the coated concrete are compared with the carbonation resistance of the uncoated plain concrete. The carbonation depth was determined using a phenolphthalein pH-indicator. A clear reduction in carbonation was observed when the mortar was applied. The validity of the accelerated testing method, which consists of placing the mortar in a high carbon dioxide concentration chamber for a controlled time, and carbonation coefficients to assess coating effectiveness are discussed. The use of 100% carbon dioxide is highly questionable for accelerated carbonation testing.     

Instability localization with artificial neural networks (ANNs)

The aim of this piece of research is to investigate the potential of artificial neural networks (ANNs) for tackling the problem of instability localization. The instability is modeled by a variable strength absorber (point-source) in a two-dimensional bare reactor model with a one neutron-energy group. The proposed approach constitutes an exercise in simplicity in that: (1) an arbitrarily simplified model is employed for ANN training and validation; (2) few training and validation patterns of low complexity are utilized; (3) the ANN inputs are derived directly from the neutron noise signals, the proposed location of instability is given on-line via an uncomplicated combination of ANN outputs; (4) the ANN architecture is independent of the number of possible locations of instability. In fact, unlike previous approaches which employ hundreds of outputs (one for each fuel assembly), only two ANN outputs are employed representing the X- and Y-coordinates (location) of instability; (5) the responses of only a few detectors are employed; (6) a measure of confidence in the prediction is assigned. The results of ANN testing, which is performed on patterns from both actual and simplified models, are reported and analyzed.