Dataset Information

Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization.

ABSTRACT: This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. First, we introduced the BP neural network algorithm based on deep learning. Second, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm was modified by using information entropy, and the modified algorithm was simulated and tested in five test functions. Then, based on the BP neural network and MTLBO algorithm, we established the MTLBO-BP neural network prediction model and tested its performance. Finally, based on the MTLBO-BP neural network prediction model, MATLAB software was used to establish an intelligent model of the risk management system during the construction of prefabricated building projects, and the example verification was performed. In addition, the MTLBO algorithm was verified by test function simulation and established that global searchability is stronger than the TLBO algorithm. Of note, it is not easy to fall into a local optimum. The test results of the MTLBO-BP neural network prediction model revealed that the prediction model converges faster and exerts a better prediction effect. The example verification of the intelligent model of the risk management system during the construction of prefabricated building projects established in this study revealed that the algorithm proposed is more accurate in the reliability and cost prediction of the risk management of prefabricated building projects. Moreover, the algorithm proposed provides theoretical support for intelligent management and decision-making of prefabricated building projects. Overall, this study validates that this algorithm is essential for construction project management, decision-making, and quality assurance.

SUBMITTER: Liu H

PROVIDER: S-EPMC7367478 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization.

Liu Huazan H He Yukang Y Hu Qichao Q Guo Jianfei J Luo Lan L

PloS one 20200717 7

This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. First, we introduced the BP neural network algorithm based on deep learning. Second, the traditional Teachi ...[more]

PMID: 32678855

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Project description:There are multiple factors that have been studied for talent identification (TI) with regard to sport performance, such as physical and physiological parameters; psychological, social, and contextual parameters; and technical–tactical parameters. However, despite the importance of these indicators for reaching the elite, new trends seem to assure that one of the key elements in a young player is decision making (DM). Thus, in the last decades, research DM in young players has increased. Nevertheless, very little has been done in relation with DM and talented players. The purpose of this study was to analyze and compare the effectiveness, number, and duration of DM units (DMUs) of three groups of talented young players in U-10, U-12, and U-14 levels. Ninety-seven youth players participated in the study. A total of 1,087 actions were analyzed. The Nomination Scale for Identifying Football Talent was utilized to screen the talent pool (N = 18), and the Game Performance Evaluation Tool was used for analyzing the 1,087 actions completed. The results showed that the effectiveness has to be more than 80% for children to be considered talented. Moreover, a greater effectiveness of DMUs was shown in older age groups. The game speed also increased with age. It was revealed that U-12 did not follow the progression in the decisional demands in the formative stages. It is highlighted, therefore, the necessity of reviewing the organizational aspects in the U-12 age group, related mainly to the size of the pitch and the number of players, because it does not follow the same progression in regard to decisional demands. Future studies should follow this study with the U-16, U-18, and U-23 age groups, with the purpose of knowing the effectiveness, duration, and number of DMUs in older age groups. Furthermore, policy makers and teachers/coaches from both educational and soccer context must take these results into account, with the purpose of adjusting the teaching and learning process of talented children in sport.

Project description:BackgroundSurgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.MethodsCochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsRequirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.ConclusionTraditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Dataset Information

Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization.

Publications

Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization.

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