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

Predicting the Significant Characteristics of Concrete Containing Palm Oil Fuel Ash

Palm Oil Fuel Ash (POFA) is used as a supplementary cementitious material in concrete. Using different percentages of POFA leads to a non-linear variation among the characteristics of concrete. This study aims at developing an empirical model to predict the compressive strength of concrete using POFA as a cement replacement material and other properties of the concrete such as the slump and modulus of elasticity using an artificial neural network. Mixtures of concrete were selected with water-to-binder ratios of 0.50, 0.55 and 0.60, and 10%, 20%, 30% and 40% of the cement content was POFA. The 28-day compressive strength was tested, and the experimental results show that 0%–20% of POFA inclusion in the concrete mixtures has the most positive effects on the compressive strength. Then, a three-layer feed forward-back propagation ANN model with three inputs and three outputs was developed. Finally, the best architecture for the model was trained, tested and validated

Where the Gaps Lie: Ten Years of Research into Collaboration on BIM-Enabled Construction Projects

A BIM-enabled Construction Project (BIMCP) refers to a project involving relevant BIM tools to generate, exchange and manage project data between project participants. Success in delivering BIMCPs largely relies on how effective project members collaborate. As a result, collaboration on BIMCP has become a growing field of research while a review of studies on collaboration on BIMCPs is still missing. To address this gap, this paper presents the findings of a systematic review on studies devoted to collaboration on BIMCPs over the past 10 years (2006-2016). To this end, 208 studies published in 12 ICT-oriented journals in the construction context are thoroughly reviewed. The findings bring to light that studies on collaboration on BIMCPs are sporadic, isolated and focus on narrowed, limited and disjointed areas associated with collaboration. The study contributes to the field through highlighting the gaps of the existing literature on the topic. This provides a stepping stone to direct future inquiries that target collaboration on BIMCPs