Цель работы − разработка методических рекомендаций по проведению проверки цифровой информационной модели раздела АР, КР и ПБ формата ifc для ГАУ СО «Управление государственной экспертизы». Для достижения поставленной цели были поставлены следующие задачи: построить модель и наполнить ее требуемой информацией, используя возможности ПО Revit; выделить основные принципы подготовки ЦИМ к экспертизе; проверить информацию из модели в формате IFC с помощью ПО Tangl Control. Сформировать проверки валидации и верификации; сформировать методические рекомендации, описывающие проведение проверки ЦИМ АР и КР в ПО Tangl Control экспертизой. / The purpose of the work is to develop methodological recommendations for checking the digital information model of the section AR, KR and PB in the ifc format for the State Autonomous Institution SO “Office of State Expertise”. To achieve this goal, the following tasks were set: build a model and fill it with the required information using the capabilities of Revit software; highlight the basic principles of preparing CIM for examination; check the information from the model in IFC format using Tangl Control software. Create validation and verification checks; create methodological recommendations describing the verification of CIM AR and KR in Tangl Control software by expert examination.

Diploma thesis deals with comparing of current awards by Classification of building structures and works and awards by Functional parts. It is possible to calculate Building life cycle costs by Functional parts. This is definately the advantage of the system because the calculation cannot be done by Classification of building structures and works. A new law on public procurement deals with Building life cycle costs as an evaluation criterion. So it is recommended to use Functional parts awards. The other goal of the thesis is to find bridges between Functional parts and the issue of BIM. BIM is a trend of global construction industry.

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.