A synthetic player for Ayὸ board game using alpha-beta search and learning vector quantization

Game playing especially, Ayὸ game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage the significant interest of users. This study presents a synthetic player (Ayὸ) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm

Template Matching Based Sign Language Recognition System for Android Devices

An android based sign language recognition system for selected English vocabularies was developed with the explicit objective to examine the specific characteristics that are responsible for gestures recognition. Also, a recognition model for the process was designed, implemented, and evaluated on 230 samples of hand gestures.  The collected samples were pre-processed and rescaled from 3024 ×4032 pixels to 245 ×350 pixels. The samples were examined for the specific characteristics using Oriented FAST and Rotated BRIEF, and the Principal Component Analysis used for feature extraction. The model was implemented in Android Studio using the template matching algorithm as its classifier. The performance of the system was evaluated using precision, recall, and accuracy as metrics. It was observed that the system obtained an average classification rate of 87%, an average precision value of 88% and 91% for the average recall rate on the test data of hand gestures.  The study, therefore, has successfully classified hand gestures for selected English vocabularies. The developed system will enhance the communication skills between hearing and hearing-impaired people, and also aid their teaching and learning processes. Future work include exploring state-of-the-art machining learning techniques such Generative Adversarial Networks (GANs) for large dataset to improve the accuracy of results. Keywords— Feature extraction; Gestures Recognition; Sign Language; Vocabulary, Android device

Two-stage capacity optimization approach of multi-energy system considering its optimal operation

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in commercial buildings. Most of previous capacity design approaches are formulated based upon conventional operating schedules, which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system. Therefore, a two-stage capacity optimization approach is proposed for the multi-energy system with its optimal operating schedule taken into consideration. To demonstrate the effectiveness of the proposed capacity optimization approach, it is tested on a renewable energy fuelled multi-energy system in a commercial building. The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit, heat recovery unit, heat exchanger, absorption chiller, electric chiller, biomass boiler, building integrated photovoltaic and photovoltaic thermal hybrid solar collector. The variable efficiency owing to weather condition and part-load operation is also considered. Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization, respectively. The two optimization stages are interrelated; thus, the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively. With the adoption of the proposed novel capacity optimization approach, there is a 14% reduction of year-round biomass consumption compared to one with the conventional capacity design approach

Benchmarks for energy access: Policy vagueness and incoherence as barriers to sustainable electrification of the global south

© 2019 The unavailability of tangible policy benchmarks continues to mitigate against sustainable electrification in the global south. Furthermore, incoherent policy benchmarks as to what should constitute clean energy allow for varying interpretations and divergent options in electrifying households across the global south. The multiplicity of policies to deepen access to improved energy services in the global south notwithstanding, ‘success’ is not in sight until definite and uniform benchmarks guide the roll-out of electrification schemes

Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads

Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary energy consumption and carbon emission during building operation. Due to the complex energy nature of the building, accurate day-ahead prediction of heating, cooling, lighting loads and BIPV electrical power production is essential in building energy management. Owing to the changing metrological conditions, diversity and complexity of buildings, building energy load demands and BIPV electrical power production is highly variable. This may lead to poor building energy management, extra primary energy consumption or thermal discomfort. In this study, three machine learning-based multi-objective prediction frameworks are proposed for simultaneous prediction of multiple energy loads. The three machine learning techniques are artificial neural network, support vector regression and long-short-term-memory neural network. Since heating, cooling, lighting loads and BIPV electrical power production share similar affecting factors, it is computational time saving to adopt the proposed multi-objective prediction framework to predict multiple building energy loads and BIPV power production. The ANN-based predictive model results in the smallest mean absolute percentage error while SVM-based one cost the shortest computation time

Salvaging building materials in a circular economy: A BIM-based whole-life performance estimator

© 2017 The Author(s) The aim of this study is to develop a BIM-based Whole-life Performance Estimator (BWPE) for appraising the salvage performance of structural components of buildings right from the design stage. A review of the extant literature was carried out to identify factors that influence salvage performance of structural components of buildings during their useful life. Thereafter, a mathematical modelling approach was adopted to develop BWPE using the identified factors and principle/concept of Weibull reliability distribution for manufactured products. The model was implemented in Building Information Modelling (BIM) environment and it was tested using case study design. Accordingly, the whole-life salvage performance profiles of the case study building were generated. The results show that building design with steel structure, demountable connections, and prefabricated assemblies produce recoverable materials that are mostly reusable. The study reveals that BWPE is an objective means for determining how much of recoverable materials from buildings are reusable and recyclable at the end of its useful life. BWPE will therefore provide a decision support mechanism for the architects and designers to analyse the implication of designs decision on the salvage performance of buildings over time. It will also be useful to the demolition engineers and consultants to generate pre-demolition audit when the building gets to end of its life

Investigating profitability performance of construction projects using big data: A project analytics approach

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implications for the efficient planning and delivery of construction projects. Setting up right profit margins and its continuous tracking as projects progress are vital management tasks that require data-driven decision support. Existing profit estimation measures use a company or industry wide benchmarks to guide these decisions. These benchmarks are oftentimes unreliable as they do not factor in project-specific variations. As a result, projects are wrongly estimated using uniform rates that eventually end up with entirely unusual margins either due to underspends or overruns. This study proposed a project analytics approach where Big Data is harnessed to understand the profitability distribution of different types of construction projects. To this end, Big Data architecture is recommended, and a prototype implementation is shown to store and analyse large amounts of projects data. Our data analysis revealed that profit margins evolve, and the profitability performance varies across several project attributes. These insights shall be incorporated as knowledge to machine learning algorithms to predict project margins accurately. The proposed approach enabled the fast exploration of data to understand the underlying pattern in the profitability performance for different types of construction projects

Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy

© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire lifecycle. In this study therefore, a disassembly and deconstruction analytics system is developed to provide buildings’ end-of-life performance assessment from the design stage. The system architecture builds on the existing building information modelling capabilities in managing building design and construction process. The architecture is made up of four different layers namely (i) Data storage layer, (ii) Semantic layer, (iii) Analytics and functional models layer and (iv) Application layer. The four layers are logically connected to function as a single system. Three key functionalities of the disassembly and deconstruction analytics system namely (i) Building Whole Life Performance Analytics (ii) Building Element Deconstruction Analytics and (iii) Design for Deconstruction Advisor are implemented as plug-in in Revit 2017. Three scenarios of a case study building design were used to test and evaluate the performance of the system. The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy. The disassembly and deconstruction analytics system would ensure that buildings are designed with design for disassembly and deconstruction principles that guarantee efficient materials recovery in mind. The disassembly and deconstruction analytics tool could also serve as a decision support platform that government and planners can use to evaluate the level of compliance of building designs to circular economy and sustainability requirements

Cloud computing in construction industry: Use cases, benefits and challenges

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive and operational advantage, the diffusion of the technologies in the industry follows a steep curve. This study therefore highlights the current contributions and use cases of cloud computing technologies in construction practices. As such, a systematic review was carried out using ninety-two (92) peer-reviewed publications, published within a ten-year period of 2009-2019. A key highlight of the research findings is that cloud computing is an innovation delivery enabler for other emerging technologies (building information modelling, internet of things, virtual reality, augmented reality, big data analytics, mobile computing) in the construction industry. As such, this paper brings to the fore, current and future application areas of cloud computing vis-à-vis other emerging technologies in the construction industry. The paper also identifies barriers to the broader adoption of cloud computing in the construction industry and discusses strategies for overcoming these barriers

Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis

In response to the gradual degradation of natural sources, there is a growing interest in adopting renewable resources for various building energy supply. In this study, a comprehensive life cycle assessment approach is proposed for a renewable multi-energy system (MES) to evaluate its primary energy consumption, economy cost and carbon emission from cradle to grave. The MES, consisting of passive side and active side, is fully driven by renewable energy including solar, wind and biomass. On the passive side, building integrated photovoltaic, solar collector and wind turbines are adopted. On the active side, the biomass-fuelled combined cooling heating and power system (CCHP) serves as the primary energy supplier. The electric compression chiller and biomass boiler are adopted when the thermal energy from the CCHP system is not sufficient, while electricity is imported from the city power grid when the electricity demand is low. A representative office building in the United Kingdom and real-life inventory data is adopted to demonstrate the effectiveness of the proposed life cycle assessment approach. Through life cycle assessment, the advantages and disadvantages of the MES are compared with the reference CCHP system and conventional separate system in view of life cycle primary energy consumption, economy cost and carbon emission. Moreover, to gain an insight into the life cycle performance, the sensitivity analysis is conducted on the rated capacity of the power generation unit, climate zones, life span, recycle ratio and interest rate. The life cycle cost of MES is relatively higher than the conventional separate system mainly owing to the high construction cost of BIPV, wind turbine, solar collector and biomass feedstock. However, its life cycle primary energy consumption and carbon emission are much lower. It is believed that the proposed life cycle assessment approach can provide useful guidelines for government in policymaking and for building engineers in retrofitting works