Active BIM with artifical intelligence for energy optimisation in buildings

University of Technology Sydney. Faculty of Design, Architecture and Building.Using Building Information Modelling (BIM) can expedite the Energy Efficient Design (EED) process and provide the opportunity of testing and assessing different design alternatives and materials selection that may impact on energy performance of buildings. However, the lacks of; intelligent decision making platforms, ideal interoperability and inbuilt practices of optimisation methods in BIM hinder the full diffusion of BIM into EED. This premise triggered a new research direction known as the integration of Artificial Intelligence (AI) into BIM-EED. AI can develop and optimise EED in an integrated platform of BIM to represent an alternative solution for building design. But, very little is known about achieving it. Hence, an exhaustive literature review was conducted on BIM, EED and AI and the relevant gaps, potentials and challenges were identified. Accordingly, the main goal for this study was set to optimise the energy efficiency at an early design stage through developing an AI-based active BIM in order to obtain an initial estimate of energy consumption of residential buildings and optimise the estimated value through recommending changes in design elements and variables. Therefore, a sequential mixed method approach was designated in which it entailed conducting a preliminary qualitative method to serve the subsequent quantitative phase. This approach was started with a comprehensive literature review to identify variables applicable to EED and the application of a three-round Delphi to further identify and prioritise the significant variables in the energy consumption of residential buildings. A total of 13 significant variables was achieved and factualised with simulation method to first; generate the building energy datasets and second; simulate AI algorithms to investigate their functionality for energy optimisation. The research was followed with developing the integration framework of AI and BIM; namely AI-enabled BIM-inherited EED to optimise the interdisciplinary data of EED in the integration of BIM with AI algorithm packages. Finally, the functionality of the developed framework was verified using a real residential building and via running comparative energy simulation pre and post-framework application (baseline and optimized case). The outcomes indicated around 50% reduction in the electricity energy consumption and 66% saving in the annual fuel consumption of the case study. Enhancing BIM applicability in terms of EED optimisation, shifting the current practice of post-design energy analysis, mitigating the less integrated platform and lower levels of interoperability are the main significant outcomes of this research. Ultimately, this research heads toward the higher diffusion levels of BIM and AI into EED which contributes significantly to the current body of knowledge and its research and development effects on the industry

Augmented reality in architecture and construction education: state of the field and opportunities

Over the past decade, the architecture and construction (AC) industries have been evolving from traditional practices into more current, interdisciplinary and technology integrated methods. Complex and intricate digital technologies and mobile computing such as simulation, computational design and immersive technologies, have been exploited for different purposes such as reducing cost and time, improving design and enhancing overall project efficiency. Immersive technologies and augmented reality (AR), in particular, have proven to be extremely beneficial in this field. However, the application and usage of these technologies and devices in higher education teaching and learning environments are yet to be fully explored and still scarce. More importantly, there is still a significant gap in developing pedagogies and teaching methods that embrace the usage of such technologies in the AC curricula. This study, therefore, aims to critically analyse the current state-of-the-art and present the developed and improved AR approaches in teaching and learning methods of AC, addressing the identified gap in the extant literature, while developing transformational frameworks to link the gaps to their future research agenda. The conducted analysis incorporates the critical role of the AR implications on the AC students’ skillsets, pedagogical philosophies in AC curricula, techno-educational aspects and content domains in the design and implementation of AR environments for AC learning. The outcomes of this comprehensive study prepare trainers, instructors, and the future generation of AC workers for the rapid advancements in this industry

Developing a hybrid model of prediction and classification algorithms for building energy consumption

Artificial intelligence algorithms have been applied separately or integrally for prediction, classification or optimization of buildings energy consumption. However, there is a salient gap in the literature on the investigation of hybrid objective function development for energy optimization problems including qualitative and quantitative datasets in their constructs. To tackle this challenge, this paper presents a hybrid objective function of machine learning algorithms in optimizing energy consumption of residential buildings through considering both continuous and discrete parameters of energy simultaneously. To do this, a comprehensive dataset including significant parameters of building envelop, building design layout and HVAC was established, Artificial Neural Network as a prediction and Decision Tree as a classification algorithm were employed via cross-training ensemble equation to create the hybrid function and the model was finally validated via the weighted average of the error decomposed for the performance. The developed model could effectively enhance the accuracy of the objective functions used in the building energy prediction and optimization problems. Furthermore, the results of this novel approach resolved the inclusion issue of both continuous and discrete parameters of energy in a unified objective function without threatening the integrity and consistency of the building energy datasets

The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games

Nowadays, Evolutionary Game Theory which studies the learning model of players,has attracted more attention than before. These Games can simulate the real situationand dynamic during processing time. This paper creates the Evolutionary MarkovGames, which maps players’ strategy-choosing to a Markov Decision Processes(MDPs) with payoffs. Boltzmann distribution is used for transition probability andthe General Regression Neural Network (GRNN) simulating the strategy-choosing inEvolutionary Markov Games. Prisoner’s dilemma is a problem that uses the methodand output results showing the overlapping the human strategy-choosing line andGRNN strategy-choosing line after 48 iterations, and they choose the same strate-gies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lowerthan similar work and shows a better re

Thermal comfort analysis of earth-sheltered buildings: The case of meymand village, Iran

Vernacular buildings are known for their localized passive settings to provide comfortable indoor environment without air conditioning systems. One alternative is the consistent ground temperature over the year that earth-sheltered envelopes take the benefit; however, ensuring annual indoor comfort might be challenging. Thus, this research monitors the indoor thermal indicators of 22 earth-sheltered buildings in Meymand, Iran with a warm-dry climate. Furthermore, the observations are used to validate the simulation results through two outdoor and indoor environmental parameters, air temperature and relative humidity during the hottest period of the year. Findings indicated that the main thermal comfort differences among case studies were mainly due to their architectural layouts where the associated variables including length, width, height, orientation, window-to-wall ratio, and shading depth were optimized through a linkage between Ladybug-tools and Genetic Algorithm (GA) concerning adaptive thermal comfort model definition and could enhance the annual thermal comfort by 31%