Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

Whilst unavoidable, inspections, progress monitoring, and comparing asplanned with as-built conditions in construction projects do not readily add tangible intrinsic value to the end-users. In large-scale construction projects, the process of monitoring the implementation of every single part of buildings and reflecting them on the BIM models can become highly labour intensive and error-prone, due to the vast amount of data produced in the form of schedules, reports and photo logs. In order to address the mentioned methodological and technical gap, this paper presents a framework and a proof of concept prototype for on-demand automated simulation of construction projects, integrating some cutting edge IT solutions, namely image processing, machine learning, BIM and Virtual Reality. This study utilised the Unity game engine to integrate data from the original BIM models and the as-built images, which were processed via various computer vision techniques. These methods include object recognition and semantic segmentation for identifying different structural elements through supervised training in order to superimpose the real world images on the as-planned model. The proposed framework leads to an automated update of the 3D virtual environment with states of the construction site.

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

This paper presents a new, multi-objective method of analysing and optimising the energy processes associated with window system design in office buildings. The simultaneous consideration of multiple and conflicting design objectives can make the architectural design process more complicated. This study is based on the fundamental recognition that optimising parameters on the building energy loads via window system design can reduce the quality of the view to outside and the received daylightboth qualities highly valued by building occupants. This paper proposes an approach for quantifying Quality of View in office buildings in balance with energy performance and daylighting, thus enabling an optimisation framework for office window design. The study builds on previous research by developing a multi-objective method of assessment of a reference room which is parametrically modelled using actual climate data. A method of Pareto Frontier and a weighting sum is applied for multi-objective optimisation to determine best outcomes that balance design requirements. The Results reveal the maximum possible window to wall ratio for the reference room. The optimisation model indicates that the room geometry should be altered to achieve the lighting and view requirements set out in building performance standards. The research results emphasise 2 the need for window system configuration to be considered in the early design stages. This exploratory approach to a methodology and framework considers both building parameters and the local climate condition. It has the potential to be adopted and further refined by other researchers and designers to support complex, multi-factorial design decision-making.