Nihat Atmaca

Life-cycle assessment of post-disaster temporary housing

ABSTRACT The estimation of energy consumption and related CO2 emissions from buildings is increasingly important in life-cycle assessment (LCA) studies that have been applied in the design of more energy-efficient building construction systems and materials. This study undertakes a life-cycle energy analysis (LCEA) and life-cycle CO2 emissions analysis (LCCO2A) of two common types of post-disaster temporary houses constructed in Turkey. The proposed model includes building construction, operation and demolition phases to estimate total energy use and CO2 emissions over 15- and 25-year lifespans for container houses (CH) and prefabricated houses (PH) respectively. Energy efficiency and emission parameters are defined per m2 and on a per capita basis. It is found that the operation phase is dominant in both PH and CH and contributes 86–88% of the primary energy requirements and 95–96% of CO2 emissions. The embodied energy (EE) of the constructions accounts for 12–14% of the overall life-cycle energy consumption. The results show that life-cycle energy and emissions intensity in CH are higher than those for PH. However, this pattern is reversed when energy requirements are expressed on a per capita basis.

Flexural buckling load prediction of aluminium alloy columns using soft computing techniques

This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate.