Construction industry has a major impact on the environment that we spend most of our life. Therefore, it is important that the outcome of architectural intuition performs well and complies with the design requirements. Architects usually describe as “optimal design” their choice among a rather limited set of design alternatives, dictated by their experience and intuition. However, modern design of structures requires accounting for a great number of criteria derived from multiple disciplines, often of conflicting nature. Such criteria derived from structural engineering, eco-design, bioclimatic and acoustic performance. The resulting vast number of alternatives enhances the need for computer-aided architecture in order to increase the possibility of arriving at a more preferable solution. Therefore, the incorporation of smart, automatic tools in the design process, able to further guide designer’s intuition becomes even more indispensable. The principal aim of this study is to present possibilities to integrate automatic computational techniques related to topology optimization in the phase of intuition of civil structures as part of computer aided architectural design. In this direction, different aspects of a new computer aided architectural era related to the interpretation of the optimized designs, difficulties resulted from the increased computational effort and 3D printing capabilities are covered here in.
Abstract-The Inverse Gaussian (IG) distribution is examined for modeling the rainfall rate and slant path and terrestrial link rain attenuation. The long-term statistics of rain rate and rain attenuation are modeled using the IG distribution. The method is validated using the recommendation of International Telecommunication Union (ITU) recommendation ITU-R. P. 837 and rain rate data from the ITU Study Group 3 database (DBSG3) database for the case of rain rate. For the modeling of rain attenuation, data which are derived from two databases of DBSG3, these of Earth-space links and line-of-sight terrestrial links are used for validating the model. The results are compared to the one using the lognormal distribution. It has been shown that IG distribution could be more appropriate for modeling rainfall rate and slant path and terrestrial link rain attenuation. Finally, some useful conclusions are derived and presented in this paper.
The Cistercian order is of acoustic interest because previous research has hypothesized that Cistercian architectural structures were designed for longer reverberation times in order to reinforce Gregorian chants. The presented study focused on an archaeoacacoustics analysis of the Cistercian Beaulieu Abbey (Hampshire, England, UK), using Geometrical Acoustics (GA) to recreate and investigate the acoustical properties of the original structure. To construct an acoustic model of the Abbey, the building’s dimensions and layout were retrieved from published archaeology research and comparison with equivalent structures. Absorption and scattering coefficients were assigned to emulate the original room surface materials’ acoustics properties. CATT-Acoustics was then used to perform the acoustics analysis of the simplified building structure. Shorter reverberation time (RTs) was generally observed at higher frequencies for all the simulated scenarios. Low speech intelligibility index (STI) and speech clarity (C50) values were observed across Abbey’s nave section. Despite limitations given by the impossibility to calibrate the model according to in situ measurements conducted in the original structure, the simulated acoustics performance suggested how the Abbey could have been designed to promote sacral music and chants, rather than preserve high speech intelligibility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.