Prediction of cost and duration of building construction using artificial neural network

Ujong, Jesam Abam; Mbadike, Elvis Michael; Alaneme, George Uwadiegwu

doi:10.1007/s42107-022-00474-4

Cited by 27 publications

(8 citation statements)

References 58 publications

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“…From the statistical computation, F = 0.359 and F crit = 4.195, indicating that F crit > F; therefore, we accept that the null hypothesis has a p -value of 0.554, which is greater than the alpha value of 0.05. This indicates that there was no significant difference between the actual or laboratory-derived results and the model-predicted results [ 73 , 74 ].…”

Section: Results Discussion and Analysismentioning

confidence: 99%

Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

et al. 2023

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Pervious concrete provides a tailored surface course with high permeability properties which permit the easy flow of water through a larger interconnected porous structure to prevent flooding hazards. This paper reports the modeling of the flexural properties of quarry dust (QD) and sawdust ash (SDA) blended green pervious concrete for sustainable road pavement construction using Scheffe’s (5,2) optimization approach. The simplex mixture design method was adapted to formulate the mixture proportion to eliminate the set-backs encountered in empirical or trials and the error design approach, which consume more time and resources to design with experimental runs required to evaluate the response function. For the laboratory evaluation exercise, a maximum flexural strength of 3.703 N/mm2 was obtained with a mix proportion of 0.435:0.95:0.1:1.55:0.05 for water, cement, QD, coarse aggregate and SDA, respectively. Moreover, the minimal flexural strength response of 2.504 N/mm2 was obtained with a mix ratio of 0.6:0.75:0.3:4.1:0.25 for water, cement, QD, coarse aggregate and SDA, respectively. The test of the appropriateness of the developed model was statistically verified using the Student’ t-test and an analysis of variance (ANOVA), and was confirmed to be acceptable based on computational outcomes at the 95% confidence interval. Furthermore, the scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) were used to evaluate the morphological and mineralogical behavior of green prior concrete samples with various additive mixture compositions. The addition of QD and SDA, on the other hand, aided the creation of porous microstructures in the concrete matrix due to fabric changes in the concrete mixture, potentially aided by the formation of cementitious compounds such as calcium aluminate hydrate and calcium silicate hydrate.

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Section: Results Discussion and Analysismentioning

confidence: 99%

Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

et al. 2023

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“…In this sense, Building Information Modeling (BIM) [36], Artificial Neural Networks (ANM) [37], Machine Learning, and Analytical Networks [38], among other emerging tools and techniques for improving project performance.…”

Section: Other Useful Emerging Techniques and Tools For Project Perfo...mentioning

confidence: 99%

Performance management in construction projects: a systematic literature review

Zuleta-Castellano,

Sanabria-Ospino,

Puerta-Guardo

et al. 2023

DYNA

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The research article related to performance management in construction projects aims to review the topics currently being studied on project performance management in construction projects. In this sense, through a bibliometric analysis of the articles taken from the Scopus and Web of Science - WoS databases, where 277 unique articles resulting from the systematic review of the literature from the PRISMA methodology were found. The processing and analysis of the data were performed through the software Biblioshiny in R, Vantage Point, and VOSviewer, from which the results of this study were presented. The results reflect a growing production between 2019 and 2023, with the participation of 732 authors and 79 sources. China leads the research in project management, followed by the United States, Australia, and the United Kingdom.

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“…As projects advance and environmental factors evolve, the risk landscape may undergo continuous transformation 10 . The BP neural network’s inherent self-learning ability empowers it to iteratively update its model based on fresh data, enabling seamless adaptation to new circumstances and changes, thereby preserving the model’s real-time relevance 11 . In conclusion, this approach is poised to enhance project management efficiency and quality, mitigate risks, and foster the potential for the successful realization of scientific research projects.…”

Section: Introductionmentioning

confidence: 99%

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

Dong,

Qiu

2024

Sci Rep

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This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network. Leveraging the BP neural network’s non-linear fitting capabilities, it captures the intricate relationship between project resource demand and supply. Additionally, the model employs self-learning to continuously adapt to new scenarios based on historical data, enabling more precise resource conflict risk assessments. Finally, the model’s performance is analyzed. The results reveal that risks in scientific research project management primarily fall into six categories: material, equipment, personnel, financial, time, and organizational factors. This study’s model algorithm exhibits the highest accuracy in predicting time-related risks, achieving 97.21%, surpassing convolutional neural network algorithms. Furthermore, the Root Mean Squared Error of the model algorithm remains stable at approximately 0.03, regardless of the number of hidden layer neurons, demonstrating excellent fitting capabilities. The developed BP neural network risk prediction framework in this study, while not directly influencing resource utilization efficiency or mitigating resource conflicts, aims to offer robust data support for research project managers when making decisions on resource allocation. The framework provides valuable insights through sensitivity analysis of organizational risks and other factors, with their relative importance reaching up to 20%. Further research should focus on defining specific strategies for various risk factors to effectively enhance resource utilization efficiency and manage resource conflicts.

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Prediction of cost and duration of building construction using artificial neural network

Cited by 27 publications

References 58 publications

Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

Performance management in construction projects: a systematic literature review

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

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