Computer-based simulation software, called hygrothermal modeling has become increasingly popular and useful to predict and evaluate heat, air, vapour, and water-related performance of buildings. This research project aims to improve such modelling for wood construction through validation using specifically measured property data and field/lab performance data. The goal is to make modelling a more reliable design tool and to subsequently improve the design and construction of both mass timber construction and light wood-frame construction.
Networks are moving towards being adaptive. This means that automation will be used to replace processes which are today highly manual. This project proposes a development of knowledge in the area of algorithms required to enable adaptive networks. The project will train two PhD students to understand optical networks and devise optimization algorithms in the areas of interest. In particular, the algorithms will be devised to be fast and near-optimal to enable their implementation in the network in accordance with operatorâs goals of making the network near-optimal and adaptive.
The objective of this project is to build up an electro-biological system of high potential capacity for the removal of ammonium and phosphorus. Conventional biological treatment methods have limited capacity for removal of ammonia at higher concentrations. However, anammox bacteria have a high capacity to remove ammonium. The electro-biological treatment aims to enhance this capacity by electrically activating anammox bacteria on a fixed media.
Workspace Interferences/Collisions happen on construction jobsites when multiple resources (labor, equipment, material, etc.) don’t have enough space to coexist at the same time and will interfere with each other’s operations. These interferences affect performance, delaying the project, impacting cost, and may jeopardize the buildings’ integrity and people’s safety on site. Most of existing models simulate resources as being deterministic. However, the behavior of the crew interfering is more chaotic.
Modular and offsite construction, where a module of project or complete house is manufactured in a factory, requires a large upfront capital (working capital) investment in order to procure materials in advance of manufacturing and to deliver modules on time and on schedule. Thus, modular fabricators need to receive deposit and progress payment before the assembly process. In the eyes of a bank, a prefab house is “just materials”.
Monitoring and control earthmoving operations such as highways and dams construction; require the collection of large amounts of data. Collecting this data manually is time-consuming and it lacks accuracy, so there is a necessity for using automated data collection systems in such operations. Most of nowadays available data acquisition systems are costly back boxes, where the user can not use it based on his/her customised needs.
In the business, it is critical to understand and predict needs of customers in advance and in precision. Machine Learning and Artificial Intelligence make it possible to extract the desirable properties and predict the objective in the future. This project is interested in the implementation of this concept as a toolbox. The toolbox will consider Online Business Model Prediction Service (OBMPS) as the objective to create an environment to drive smart decisions.
Usually, any construction project begins with the planning department preparing a construction schedule that is pushed onto the project team. The big up-front plan might cause delays in the construction process because it relies on predicting a lot in the future, so you need frequent updating of this plan throughout the execution. Agile is a project management approach that includes micro-planning tools to support construction contractors in the execution of projects.
The Internet of Things (IoT) is a new emerging paradigm and is rapidly gaining ground in different applications of significant engineering importance including but not limited to smart buildings, and smart public environments. The main enabling factor of this promising paradigm is integration of identification, localization, and navigation technologies with smart hand-held devices equipped with sensing, processing, and communication capabilities.