In the information age, the role of data in any industry is getting more and more important. In the construction industry, however, experts’ experience and intuition are still key determinants in decision-making, while other industries achieve tangible improvement in paradigm shifts by adopting cutting-edge information technology. Cost and schedule controls, which are closely connected, are decisive in the success of construction project execution. While a vast body of research has developed methodologies for cost-schedule integration over 50 years, there is no method used in practice; it remains a significant challenge in the construction industry. This study aims to propose a data processing algorithm for integrated cost-schedule data management employing big data technology. It is designed to resolve the main obstacle to the practicality of existing methods by providing integrity and flexibility in integrating cost-schedule data and reducing time on building and changing databases. The proposed algorithm is expected to transform field engineers’ current perception regarding information management as one of the troublesome tasks in a data-friendly way.

The construction industry has persistently high accident rates, and major events continue despite strengthened safety management systems. This study analyzes 19,456 accident records from the national Construction Safety Management Integrated Information (CSI) system and applies a Light Gradient Boosting Machine (LightGBM) model to predict fatal versus injury outcomes. SHAP was used to identify influential factors and quantify each variable’s contribution. Fatal events represented about 5% of cases, reflecting substantial class imbalance. To address this, three oversampling methods—SMOTE, Borderline-SMOTE, and ADASYN—were tested. The ADASYN model showed the best performance (F1-score = 0.905, AUC = 0.879) and was selected as the final model. Oversampling was applied exclusively to the training folds during stratified 10-fold cross-validation on the training set. After identifying the optimal number of iterations, the model was retrained on the full training data and its final performance was evaluated on the independent test set. SHAP results indicated that Type of Accident, Accident Object, and Work Process were primary drivers of fatal outcomes, whereas Safety Management Plan and Public/Private Ownership helped lessen severity. Project Cost, Progress Rate, and Number of Workers moderated prediction strength through interactions with key variables. This study clarifies structural relationships among factors affecting accident outcomes using a LightGBM–SHAP framework that captures nonlinear interactions, supporting explainable artificial intelligence (AI)–based safety management and risk monitoring.