An Artificial Neural Network Approach for Construction Project Risk Management
Bibliographic record
Abstract
Effectively managing strategic risks within the realm of Internal Security of Establishments (ISE) is crucial for safeguarding data and preserving a company's information assets.The construction industry is notably one of the riskiest sectors, susceptible to diverse risks that can adversely impact the three crucial aspects of projects: time, cost, and quality.In the context of construction projects, inherent risks and the susceptibility to data loss are intricately linked, primarily stemming from the prevalent utilization of computerized project management systems.Throughout the project lifecycle, tasks are significantly reliant on sophisticated software tools and integrated packages.The increasing reliance on technology renders projects vulnerable to information security threats, including cyberattacks, human errors, technical failures, or natural disasters, thereby disrupting managerial processes and resulting in substantial data losses.Consequently, risk management has emerged as a pivotal area of study, demanding increased attention and focus from professionals within the construction industry.This study aims to delve into how different stakeholders perceive various types of risks, including risk information specific to construction projects.An artificial neural network (ANN) was employed to predict risk.The study identified four categories of responsibility: shared responsibility, contractor responsibility, client responsibility, and data loss responsibility.The ANN topology was optimized based on changes in mean square errors (MSE) and correlation coefficients (R 2 ).The numerical results demonstrated the model's strong performance, with a low MSE of 0.0029 and a high R 2 value of 0.9666, indicating its reliability and effectiveness in risk prediction.The findings indicated that the model suggested could serve as a viable approach to the most efficient methods of preventative risk management.By using an ANN model, the study offered a novel approach to risk management in construction projects and ISE, suggesting avenues for further research and application in similar contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".