A Decision Support System for Information Technology Policy Formulation

The implementation of an effective ICT policy requires the development of material and intellectual resources to support good decision making by humans.   In this paper, we examined and analysed Information Technology (IT) policy development process with a view to developing automated system supporting such process.  The data used for this work were obtained through purposeful interview of five professionals and experts who are familiar with IT policy formulation in Nigerian environment.  Some of the experts had earlier participated in policy design and formulation process at national level.     The Hierarchical Input Process Output (HIPO) model was used to analyse various input (contributions of professionals and experts) and output (agreed resolution of the professionals and experts) of the system.  The information obtained from the experts was represented using rule base techniques.  The overall system was designed using the Unified Modelling Language (UML) and implemented using the Visual Prolog version 7.0.  The metrics used for evaluating the system includes: processing time, decision process efficiency and cost effectiveness.   We compared the result of our system with that of the traditional manual system in use.  Our result showed that the DSS for policy formulation process enhances the decision output significantly when compared to the manual process where no DSS is used.  Moreover, the quality of policy produced by our DSS system is more consistent when compared with the manual process.

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

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

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

Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities

The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry

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

Deep learning model for demolition waste prediction in a circular economy

An essential requirement for a successful circular economy is the continuous use of materials. Planning for building materials reuse at the end-of-life of buildings is usually a difficult task because limited time are usually made available for building removal and materials recovery. In this study, deep learning models were developed for predicting the amount (in tons) of salvage and waste materials that are obtainable from buildings at the end-of-life prior to demolition. Datasets used for deep neural network model developments were extracted from 2280 building demolition records obtained from the practitioners in the UK Demolition Industry. The data was partitioned into training, testing and validation datasets in the ratio 8:1:1. Deep learning models were developed with a deep learning framework in R programming environment. The average R-squared value for the three deep learning models is 0.97 with Mean Absolute Error between 17.93 and 19.04. The models were evaluated with four scenarios of a case study building design. The results of the evaluation show that, given basic features of buildings, it is possible to predict with a high level of accuracy, the amount of materials that would be recovered from a building after demolition. The models developed will provide decision support functionalities to demolition engineers and waste management planners during the pre-demolition audit exercise

Classification-Based Ridge Estimation Techniques of Alkhamisi Methods

Following Lukman and Ayinde [9]: review and classification of methods of estimating ridge parameters into different forms and various types, this study proposed some new ridge parameter estimation using the idea of Alkhamisi et al. [1]. The performance of the techniques was evaluated by conducting Monte-Carlo experi- ments under certain conditions and compared using relative efficiency. Results show that increase in the strength of multicollinearity resulted in increase in mean square error (MSE), which decreases as the sample size increases. Furthermore, the most preferred technique is generally in the different forms in the original and square root types. Moreover, Fixed Maximum Original (FMO) for Alkhamisi et al. [1], the proposed Varying Maximum Original (VMO) for AL4, VMO for AL6 and Harmonic Mean Original (HMO) for AL5 competes favorably. Keywords Mean square error; Monte-Carlo experiment;Ridge parameter; Relative efficiency