Computational Modelling tools for the promotion of Design for Deconstruction

This Master’s thesis is focused on the ambiguous topic concept of Designing for Deconstruction and its application within the construction industry. The aim of the research is to investigate the feasibility of the concept by pointing out the main topics of interest that could influence its overall performance and propose a strategy that will assess the environmental and financial performance of the concept when applied on a new design. The strategy will also propose a scheme for utilization of the concept for managerial purposes. Furthermore, the main objective is to implement the proposed strategy into a computational modeling toolbox based on the principals of parametric and associative design. The toolbox will provide assessment of the various factors which could affect the early design stage of a project, aligning with the main requirement of Designing for Deconstruction that is to be applied in this preliminary stage of a design process. The toolbox is developed to fit the modeling platform Rhino3D (McNeel) through Grasshopper3D.Design and Construction processesStructural EngineeringCivil Engineering and Geoscience

The Theory and Practices of Evaluating Transformative Learning Processes

This paper aims at defining a framework for the most recent approaches and tools used to evaluate Transformative Learning process and its outcomes. Actually, many experiences and tools have been discussed and validated in the international debate. The literature shows that there are different approaches to validate and use these tools suggesting that the Transformative Learning Theory is interpreted in many ways. This chapter provides a critical analysis of the European and American approaches and tools used to evaluate TL processes. The objectives of the paper are: 1) showing the state of the art of assessment perspectives in TLT; 2) defining a framework to categorize them; 3) depict a possible evolution of TL assessment approaches

Operational precise irrigation for cotton cultivation through the coupling of meteorological and crop growth models.

In this paper, we tested the operational capacity of an interoperable model coupling system for the irrigation scheduling (IMCIS) at an experimental cotton (Gossypium hirsutum L.) field in Northern Greece. IMCIS comprises a meteorological model (TAPM), downscaled at field level, and a water-driven cultivation tool (AquaCrop), to optimize irrigation and enhance crop growth and yield. Both models were evaluated through on-site observations of meteorological variables, soil moisture levels and canopy cover progress. Based on irrigation management (deficit, precise and farmer’s practice) and method (drip and sprinkler), the field was divided into six sub-plots. Prognostic meteorological model results exhibited satisfactory agreement in most parameters affecting ETo, simulating adequately the soil water balance. Precipitation events were fairly predicted, although rainfall depths needed further adjustment. Soil water content levels computed by the crop growth model followed the trend of soil humidity measurements, while the canopy cover patterns and the seed cotton yield were well predicted, especially at the drip irrigated plots. Overall, the system exhibited robustness and good predicting ability for crop water needs, based on local evapotranspiration forecasts and crop phenological stages. The comparison of yield and irrigation levels at all sub-plots revealed that drip irrigation under IMCIS guidance could achieve the same yield levels as traditional farmer’s practice, utilizing approximately 32% less water, thus raising water productivity up to 0.96 kg/m3.N/