Engineering properties and microstructure of a sustainable roof tile manufactured with waste rice husk ash and ceramic sludge addition

Clay replacement with waste rice husk ash (RHA) and ceramic sludge (CS), helps to reduce the consumption of natural clay and solves the ecological issues created by waste disposal. In this study, properties of waste RHA and CS added fired clay tile were investigated, focusing on structural, durability, thermal performance as well as the water quality of the harvested run-off from fired clay roof tiles manufactured in an industrial scale plant. Tiles were cast by clay replacement with waste RHA and CS in four mixtures: 10 %RHA and 0 % CS, 10 % RHA and 10 % CS, 10 % RHA and 15 % CS, and 10 % RHA and 20 % CS (by weight). For 10 %RHA and 10 %CS tiles, dry mass was reduced by 4.9 %, compared with conventional roof tiles, promising a light weight roof tile. Roof tiles with 10 % RHA and 10 %CS showed a transverse breaking load of 1519 N, whereas that of 20 %CS tiles showed 1427 N, indicating that a further 6.5 % strength improvement can be achieved with clay replacement with a combination of two waste materials. Clay replacement with 10 % RHA and 10 % CS resulted in water absorption of 15.25 %. When increasing the clay replacement with combined waste from 10% (10 %RHA and 0%CS) to 30 % (10%RHA and 20 %CS), weight gain due to acid and alkaline attacks reduced from 3.5% to 3.0%, and from 2.2 % to 1.6 %, respectively, indicating enhanced durability performance by incorporating combined waste. High porosity, also confirmed by SEM, contributed to enhanced thermal performance: tile with 10 % RHA and 10 % CS achieved 4.4 °C temperature reduction, compared to the conventional tile. pH value and total solid concentration of run-off water were in the range of recommended values of water for agricultural purposes, ensuring that the collected run-off can be utilized as an alternative water source for potable activities.publishedVersio

Investigation of the Flexural Behavior of Preloaded and Pre-Cracked Reinforced Concrete Beams Strengthened with CFRP Plates

This paper investigates the flexural behavior of preloaded reinforced concrete (RC) beams, strengthened with Carbon Fiber Reinforced Polymer (CFRP) plates using an experimental program, analytical procedure, and Finite Element Method (FEM) simulation. The RC beams were subjected to preloads of 30%, 50% and 70% of the yielding load, prior to installation of the strengthening system. The eight RC-strengthened beams with a reinforcement configuration of 3Ø12 and two CarboDur S512 plates have been evaluated using bending tests. The failure modes of all the RC-strengthened beams were governed by the widening of flexural cracks within a constant bending zone, followed by debonding of the CFRP plates. The plates were debonding simultaneously or one plate prior to the other plate. The ultimate moment capacity is not significantly reduced while increasing preload levels from 0% to 70%. The moment capacity is increased by 70% to 80% in the CFRP strengthened beams, compared with un-strengthened beams indicating the potential of capacity enhancement that can be attained by externally bonded CFRP

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R 2) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches. </p