A low cost course information syndication system

This study presents a cost-effective, reliable, and convenient mobile web-based system to facilitate the dissemination of course information to students, to support interaction that goes beyond the classroom. The system employed the Really Simple Syndication (RSS) technology and was developed using Rapid Application Development (RAD) methodology. The design of the system was modelled using Unified Modeling Language (UML) diagrams, while its implementation was done using Java Micro Edition (JME) and “PHP: Hypertext Preprocessor” (PHP).A simulation technique was used to evaluate the proposed system performance by comparing the approach used in its design to one adopted in a similar study, using response time and bandwidthconsumption as metrics. The results obtained revealed that the performance of the proposed syndication system was better. Similarly, an experiment to investigate the students’ perception of the system was conducted, with students’ responses revealing a tremendous success of this project

Computer vision and IoT research landscape for health and safety management on construction sites

Aims: Perform a systematic review of current literature to evaluate and summarise the health and safety hazards on construction sites. Methods: Science Direct, SCOPUS and web of science databases were searched for research articles published from 2013 to 2021. From an initial search of 350 research articles, we removed the duplicate articles and carried out an analysis of the abstract and full text that focused on health, safety, hazards, behaviour, on-site health and safety and the digital technologies leaving a total of 66 studies included. Results: Computer vision and Internet of Things (IoT) are the dominant technologies for health and safety management. A comparison of the two technologies reveals that computer vision is dominant because of its non-intrusive approach to data collection; thus, supporting the scalability of computer vision approach at the expense of cost and development time. It will help to prevent on-site health and safety hazards and injuries on construction site. Conclusion: Computer vision offers non-intrusive benefits over Internet of Things (IoT); being able to detect the health and safety hazards. Computer vision has proved to be beneficial for better accuracy prediction, real time data monitoring, and model development for onsite health and safety analytics on the construction site

Performance comparison of deep learning and boosted trees for cryptocurrency closing price prediction

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks

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

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

A case-study approach to profitability assessment in fermented African locust beans (iru) production using break-even analysis

The significance of a break-even point analysis is much important to the small- scale enterprises in determining profit or loss incurred. In this study, a case study approach was used to test this concept on iru production using structured oral interviews and visual observation of a production centre in Nigeria. Data analysis was achieved using production cost, the unit quantity and break-even point analysis. The results show that the monthly average cost of N 32,237.00, the monthly average variable cost of N71,334.00, Contribution Margin Ratio of 47.06 % and break-even sales of N 68,502.00 were obtained using a traditional method of iru production (TIPP). In a mechanized method (MIPP), monthly average fixed cost of N 85,887.00, the monthly average variable cost of N 55,000.00, monthly average sales of N 141,500.00, Contribution Margin Ratio (CMR) of 61.13 % and break-even sales of N 140,499.00 were obtained. High CMR value of MIPP to TIPP signifies a higher level of safety in the enterprise. Also, the graphical method revealed that MIPP is best option to choose by the processor for the large-scale productions since the process took lesser production costs, yields more outputs and gave more profits at any point above BEP compare to TIPP counterpart. Thus, the enterprise earns more profit from iru production at any point above the equilibrium point (break-even point) with the use of a processing machine. The research could serve as a reference point to promote mechanization for improving iru production