Hybrid Artificial Neural Network and Genetic Algorithm Model for Multi-Objective Strength Optimization of Concrete with Surkhi and Buntal Fiber

Cortez

Pereira³

et al. 2021

Expert Systems

Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modeling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on this Business Analytics usage, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analyzed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modeling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.

Section: Descriptive Analyticsmentioning

confidence: 99%

Section: Literature Review Analysismentioning

confidence: 99%

Business analytics in Industry 4.0: A systematic review

Cortez

Pereira³

et al. 2021

Expert Systems

“…Number of Hidden Layers 2 (1 Hidden Layer and 1 Output Layer) Performance Indicators R, MSE and MAPE [13] The developed model was obtained using different performance indicators as shown in equation ( 3) -Pearson's Correlation Coefficient, equation ( 4) -Mean Squared Error (MSE) and equation ( 5) -Mean Absolute Percentage Error (MAPE).…”

Section: Internal Characteristics Valuementioning

confidence: 99%

Management Competency Model: Predictive Neural Network Approach for Empirical Components of Construction Project Proficiency

Frontiers in Artificial Intelligence and Applications

Jesus

Villaverde

et al. 2021

Self Cite

Management competencies are skills that incorporate the understanding, proficiencies and qualities essential for successful performance. Individuals in the top of the organizational hierarchy presents himself being an effective leader by immersing to a readily difficult activity in the project. Molding the important management competencies was found to be hard since the efficacy of a competent construction manager is dependent on countless administrative aspects. The current study intended to offer a construction management competency for human resource development in construction companies. Utilizing this competency model, the company could increase its performance capacity and productivity. This study developed a competency theory and a hybrid predictive model with specific foci on construction managers. The Management Competency Framework Assessment Instrument was developed, following an approach through factor-item analytic mode. Furthermore, this research constructs a predictive performance model using Artificial Neural Network through the factors associated to successful management performance. A sensitivity analysis was also implemented to assess the relative importance of individual factors to the effective construction performance.

“…Pearson's Correlation Coefficient (R), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) were used as performance indicators [10]. The created model's efficacy and good level performance for cost, time and quality performance rating must have extremely high R values and extremely low MSE and MAPE values [11].…”

Section: Neural Network Modellingmentioning

confidence: 99%

Hybrid Neural – Based Model for Predicting the Construction Project Performance of High – Rise Building Projects in Metro Manila, Philippines

Fernandez

Frontiers in Artificial Intelligence and Applications

Jesus

2021

Self Cite

Assessing construction project performance through adapting an innovative approach can multiply the production of high-quality project outputs which is a catalyst to the socio-economic progress of a country. Preliminary data collection was employed through a meta-cognitive analysis of reviewing related literature which directs to the backbone of qualitative information that is relevant to the periodically experienced construction performance-influencing factors and to develop an assessment questionnaire about the influencing factors affecting the project performance. The IBM SPSS program was used to verify the reliability and consistency of the fundamental statistics of the questionnaire responses of cost, time and quality performance ratings. A predictive mathematical model was developed for forecasting cost, time, and quality performance rating employing Levenberg-Marquardt training algorithm with Hyperbolic Tangent Sigmoid function. The prediction model result shows a highly satisfying performance on its variance from the substantive values and suggests a high correlation between these values. The relative importance of the factors affecting the cost, time, and quality performance rating was calculated via sensitivity analysis through connection weights using Garson’s Algorithm to view the order of influence of the parameters that have great significance to the success of a project.