• Energy Research

Abstract This article presents the analysis of the structure, energy and cost efficiency of three lightweight structural systems – wood light frames (WLF), lightweight steel frames (LGSF) and 3D sandwich (3DSP) panels – during their useful life. The structural systems focussed upon in this study are commonly used in Eastern Europe with specific reference to Turkey. The structural analysis and design was carried out using ETABS while EnergyPlus was used in the analysis of the energy consumption of the buildings. The results of the structural analysis of the three alternative construction systems show that 3DSP has better structural behaviour in terms of resistance against lateral loads. The thermal performance evaluation of the walls and ceilings shows that the WLF and LGSF walls have better insulation values (12.5% lower U-value) while the roof construction of the 3DSP has much better insulation performance (70% lower U-value). Moreover, the building designed with 3DSP requires 11% less energy for total heating and cooling during one year. The information for the building industry in Turkey shows that the cost of construction for 3DSP construction is 34.6% lower than for WLF and 27.7% lower than LGSF.

Abstract Energy efficiency is the predominant criterion in green building indices, which, in turn, contributes to sustainable development. One of the materials commonly used in the insulation of buildings is foamed concrete. This investigation presents the main objective of the experimental results concerning the thermal conductivity of oil palm shell foamed geopolymer concrete (OPSFGC), utilizing waste materials such as low-calcium fly ash (FA) and palm oil fuel ash (POFA) as cementitious materials, and oil palm shell (OPS) as lightweight coarse aggregate (LWA). Three OPSFGC mixtures with densities of 1300, 1500 and 1700 kg/m3 were prepared using an artificial foaming agent; a control mix without foam and conventional materials – block and brick – were used for comparison. The test results on the mechanical and transport properties are also discussed. The thermal conductivity of OPSFGC13 of about 0.47 W/mK was 22% and 48% lower than the conventional wall materials, block and brick, respectively. OPSFGC, with a density of 1300 and 1500 kg/m3, could be categorized as structural and insulating concrete, Class-II, whereas OPSFGC with a density of 1700 kg/m3 is classified as Class-I structural grade concrete with a compressive strength and thermal conductivity of about 30 MPa and 0.58 W/mK, respectively.

Reinforced concrete (RC) structures require strengthening for numerous factors, such as increased load, modification of the structural systems, structural upgrade or errors in the design and construction stages. The side near-surface mounted (SNSM) strengthening technique with glass fiber-reinforced polymer (GFRP) bars is a relatively new emerging technique for enhancing the flexural capacities of existing RC elements. Nine RC rectangular beams were flexurally strengthened with this technique and tested under four-point bending loads until failure. The main goal of this study is to optimize the structural capacity of the RC beams by varying the amount of strengthening reinforcement and bond length. The experimental test results showed that strengthening with SNSM GFRP bars significantly enhanced the flexural responses of the specimens compared with the control specimen. The first cracking and ultimate loads, energy absorption capacities, ductility and stiffness were remarkably enhanced by the SNSM technique. It was also confirmed that the bond length of the strengthened reinforcement greatly influences the energy absorption capacities, ductility and stiffness. The effect of the bond length on these properties is more significant compared to the amount of strengthening reinforcement.

In this study, soft computing methods are designed and adapted to estimate energy consumption of the building according to main building envelope parameters such as material thicknesses and insulation K-value. In order to predict the building energy consumption, novel intelligent soft computing schemes, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) are used. The polynomial, linear, and radial basis function (RBF) is applied as the kernel function of the SVR to estimate the optimal energy consumption of buildings. The performance of proposed optimizers is confirmed by simulation results. The SVR results are compared with the ANFIS, artificial neural network (ANN), and genetic programming (GP) results. The computational results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach in comparison to the SVR estimation. Based on the simulation results, the effectiveness of the proposed optimization strategies is verified. The data used in soft computing were obtained from 180 simulations in EnergyPlus for variations of building envelope parameters.

The reactivity effect of calcium carbonate, present in ground oyster shells and limestone filler, on the formation of carboaluminate phases in ground granulated blast furnace slag blended cement pastes was reported in this paper. Six different binary and ternary blended cement pastes were prepared using ground granulated blast furnace slag, ground oyster shells and limestone filler with different replacement levels (from 5 to 35%). The carboaluminate formation was assessed and quantified directly using X-ray diffraction (XRD), and indirectly by following the aluminate phase’s reaction (heat flow) and consumed calcium carbonate using Isothermal Calorimetry (IC) and Thermogravimetric Analysis (TGA), respectively. Further, the overall reaction degree calculated based on TGA results and the compressive strength were determined to support the findings obtained. The results revealed that the calcium carbonate present in ground oyster shells is more reactive when compared to that present in limestone filler, where more formed hemi- and monocarboaluminate phases were observed in mixtures containing ground oyster shells. An enhancement in compressive strength and overall reaction degree was observed by adding 5% ground oyster shells as cement replacement.

Abstract In this investigation, agro-solid waste materials from the palm oil industry such as oil palm shell (OPS) and palm oil fuel ash (POFA) were utilized to replace conventional concrete-making materials to produce lightweight concrete. The OPS was used as replacement for conventional coarse aggregate while ground POFA was used at partial cement replacement levels of up to 25%. The inclusion of POFA up to 25% did not detrimentally affect the fresh concrete properties while the use of POFA at 10–15% replacement levels improved the compressive strength of OPS concrete (OPSC). Although there was little effect of POFA on the modulus of elasticity, increased POFA replacement levels led to reduction in both the splitting and flexural tensile strengths of OPSC. The evaluation of the cost and eco-efficiencies showed that inclusion of 10% POFA gave the most optimum performance in terms of the sustainability of the OPSC.

Abstract The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.