Assessing construction and demolition waste on masonry works to avoid future generation

The difficulty of dealing with construction and demolition waste (CDW) on construction sites is not new and continues to be a significant environmental problem. Currently the CDW collection system in Spain is done in a decentralized manner by each sub-contracted company, being necessary to implement effective waste management measures ensuring a correct management and minimization. During the last years several measures have been launched in order to improve and encourage the reuse and recycling of CDW. A widespread solution for CDW recovery is using them as a landscaping aggregate or for road bases and sub-bases. However, measures encouraging onsite prevention still need to be enhanced. This paper studies the major work stage generating CDW and analyses the categories of CDW produced during its execution. For this, several real building sites have been analysed in order to quantify the estimation of CDW generated. Results of this study show that a significant contributor to the CDW generation on building construction sites in Spain are the masonry works. Finally, a Best Practices Manual (BPM) is proposed containing several strategies on masonry works aimed not only at CDW prevention, but also at improving their management and minimization. The use of this BPM together with the Study and Plan of CDW management --required by law--, promotes the environmental management of the company, favouring the cohesion of the construction process organization at all stages giving rise to establishing responsibilities in the field of waste and providing a greater control over the process. Keywords: construction and demolition waste, management, masonry works, good practice measures, prevention.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.