• Steel and Composite Structures
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• v.7 no.6
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• pp.457-468
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• 2007

Analytical and experimental investigation on dynamic properties of extra lightweight concrete sandwich beams reinforced with various lay ups of carbon reinforced epoxy polymer composites (CFRP) are discussed. The lightweight concrete used in the core of the sandwich beams was made up of extra lightweight aggregate, Lica. The density of concrete was half of that of the ordinary concrete and its compressive strength was about $100Kg/cm^2$. Two extra lightweight unreinforced (control) beams and six extra lightweight sandwich beams with various lay ups of CFRP were clamped in one end and tested under an impact load. The dimension of the beams without considering any reinforcement was 20 cm ${\times}$ 10 cm ${\times}$ 1.4 m. These were selected to ensure that the effect of shear during the bending test would be minimized. Three other beams, made up of ordinary concrete reinforced with steel bars, were tested in the same conditions. For measuring the damping capacity of sandwich beams three methods, Logarithmic Decrement Analysis (LDA), Hilbert Transform Analysis (HTA) and Moving Block Analysis (MBA) were applied. The first two methods are in time domain and the last one is in frequency domain. A comparison between the damping capacity of the beams obtained from all three methods, shows that the damping capacity of the extra lightweight concrete decreases by adding the composite reinforced layers to the upper and lower sides of the beams, and becomes most similar to the damping of the ordinary beams. Also the results show that the stiffness of the extra lightweight concrete beams increases by adding the composite reinforced layer to their both sides and become similar to the ordinary beams.

• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.815-821
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• 2011

This study aims to estimate the biological decomposition capacity of MPC(Microorganism Porous-Concrete). MPC has specific surface area formed by inside pores, and bio compound was added to those pores to reduce pollutants loading. To evaluate the water purification capacity of MPC, we carried out the comparative studies using different media types [GPC(General Porous-concrete), CPC(Compound porous-concrete), LPC(Lightweight aggregate porous-concrete)] under the condition of CFSTR, and different retention times (30, 60 and 120 min). We also estimated the purification capacity of MPC under different concentrations of pollutant loadings. The MPC showed higher efficiency in water purification function than other conventional porous concretes with efficient decrease rates of SS, BOD, COD, and nutrient concentrations. In the comparison experiment for different retention times, MPC showed the highest removal efficiency for all tested pollutants in the longest retention time(120 min). In the long period test, the removal efficiencies of MPC concrete were high until 100 days after the set up of the operation, but began to decrease. Outflow flux was invariable compared with inflow flux so that extra detention time for media fouling such as back washing is not needed. But the results suggested that appropriate management is necessary for long-term operation of MPC. As the final outcome, MPC using bio organisms is considered to be efficient for stream water purification when they used as substrates for artificial river structure.