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"safety prediction"
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The science of an earthquake
This book discusses the science behind earthquakes and their effects. The chapters examine notable earthquakes in history, explain why earthquakes occur, and show how scientists and engineers are working to understand earthquakes and build damage-resistant structures. Diagrams, charts, and photos provide opportunities to evaluate and understand the scientific concepts involved.
Book
Thermal hazard evaluation of N-guanylurea dinitramide (GUDN) by using kinetic-based simulation approach
To promote the practical application of N-guanylurea dinitramide (GUDN), it is necessary to identify the thermal kinetics and evaluate thermal hazards of GUDN under various environmental conditions. In this study, we present that the thermal decomposition of GUDN is a typical autocatalytic reaction and the model-based kinetics was established by simultaneous fitting of a series of nonisothermal DSC data at different heating rates, which can be described as a generalized autocatalytic model, expressed as dαdt=2.29×1023exp-225240/RT1-α1.76α1.47+0.59e-18300RT . The reaction model exhibits a reasonable fitting to the experimental results with a high correlation coefficient R2 of 0.9994. Based on the established kinetic model, important thermal safety indicators, such as the time to conversion limit, adiabatic time to maximum rate, and self-accelerating decomposition temperature, were simulated, providing important basis concerning the thermal hazard of GUDN in practical applications.
Journal Article
Uncertainty prediction of mining safety production situation
In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to
Low
,
R
, and
Up
three granular parameter sequences, according to the triangular fuzzy number; then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of
Low
,
R
, and
Up
are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining, and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of
Low
,
R
, and
Up
sequences were 9.472 %, 16.671 %, and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity, and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.
Journal Article
Design of building construction safety prediction model based on optimized BP neural network algorithm
In order to solve the safety problem of the construction industry, the construction safety prediction model based on the optimized BP neural network algorithm is designed in this study. First, the characteristics of the construction industry were analyzed. As a labor-intensive industry, the construction industry is characterized by numerous factors such as large investment, long construction period and complicated construction environment. Due to the increasingly serious security problem, widespread concern over such problem has been aroused in society. Second, the problem of building construction safety management was summarized, six influencing factors were explored and a building construction safety prediction model based on rough set-genetic-BP neural network was established. Finally, the model was validated by a combination of multiparty consultation, empirical analysis and model comparison. The results showed that the model accurately predicted the risk factors during the construction process and effectively reduced casualties. Therefore, the model is feasible, effective and accurate.
Journal Article
Advancing chemical safety prediction: an integrated GNN framework with DFT-augmented cyclic compound solution
The rapid proliferation of chemical substances presents significant challenges in assessing their safety–critical physicochemical properties. This study presents an integrated approach using Graph Neural Networks (GNNs) to predict three crucial properties for chemical safety assessment: Heat of Combustion (HoC), Vapor Pressure (VP), and Flashpoint. Leveraging comprehensive datasets of 4780, 3573, and 14,696 compounds respectively, we developed a unified prediction model that outperforms existing approaches. Our model achieves mean absolute errors of 126 J/mol (R
2
= 0.993) for HoC, 0.617 log units (R
2
= 0.898) for VP, and 14.42 °C (R
2
= 0.839) for Flashpoint, representing notable improvements over conventional methods. Through detailed analysis, we identified and addressed a specific challenge in predicting HoC for cyclic compounds by implementing a hybrid approach combining DFT calculations and Random Forest modeling. This specialized treatment expanded our cyclic compound dataset from 12 to 55 compounds and achieved an R
2
of 0.918 for these traditionally challenging structures. The model was integrated into a real-time prediction system using Flask, allowing users to input chemical structures through SMILES notation or direct drawing. The system includes features for comparing predictions with experimental data and benchmarking against common industrial chemicals (acetone, n-hexane, and n-decane), enhancing its practical utility in emergency response scenarios. Our approach provides a robust, unified solution for predicting multiple safety–critical properties simultaneously, addressing a crucial need in chemical safety assessment and emergency response planning.
Scientific contribution
Overall, this study provides an integrated framework that deploys three GNN-based prediction models within a common architecture and a real-time prediction system. For cyclic compounds, which exhibit systematic prediction challenges under the GNN framework, we incorporate a targeted alternative modeling strategy to improve predictive reliability, thereby enhancing the practical applicability of machine-learning approaches to chemical safety assessment.
Journal Article
Slope stability prediction based on AutoML for multiple failure mechanisms
Slope stability prediction is one of the most critical tasks in geotechnical and transportation engineering projects. Accurate prediction of slope stability is of great significance for the initial design of slope projects and disaster prevention. Currently, prediction methods combining data-driven approaches with AutoML (Automated Machine Learning) have achieved substantial research results in predicting the Factor of Safety (
FOS
) of slopes. In order to explore the generalization ability of AutoML models in predicting different failure mechanisms, this paper, for the first time, uses the TPOT (Tree-based Pipeline Optimization Tool) model to predict the FOS of slopes with commonly occurring failure mechanisms such as circular failure, translational failure, and buckling failure in the mountainous and canyon areas of Western Sichuan, China. The reliability of the TPOT model on datasets with different failure mechanisms and feature dimensions was verified using the Feature-engine missing value indicator. The experimental results show that the TPOT model achieves an
R
2
of 0.984, 0.984, and 0.991, with
RMSE
values of 0.039, 0.054, and 0.039 for the actual engineering case test sets of the three failure mechanisms, respectively. When facing data missing situations, the
R
2
values were 0.941, 0.940, and 0.988, with
RMSE
values of 0.047, 0.099, and 0.046, still demonstrating strong predictive ability, thus validating the generalization ability and robustness of the TPOT model. Additionally, a Multi-type Factor of Safety Prediction System (MSSPS) was developed in this paper, featuring a simple and user-friendly graphical user interface (GUI) to enable field personnel to make rapid preliminary assessments of slope stability.
Journal Article
Information-Driven Rule Reduction in Belief Rule Bases for Complex System Modeling
In the analysis of complex engineering systems, managing uncertainty and optimizing information processing structures are critical for reliable state prediction. The Belief Rule Base (BRB) provides a powerful machine learning approach for integrating expert knowledge with uncertain information. However, mitigating the combinatorial complexity of BRBs through conventional structure simplification often causes unintended information loss, introducing systematic prediction biases that undermine reliability. To address the trade-off between system complexity and modeling accuracy, this study proposes an adaptive belief rule base framework integrating sensitivity analysis with posterior consistency calibration (BRB-ARR). First, an information-driven rule screening mechanism is developed to dynamically determine the pruning threshold based on optimized Mean Square Error (MSE) fluctuations. This method effectively filters redundant rules while avoiding the cognitive biases associated with fixed empirical values. Second, a low-dimensional optimization process is employed to readjust the parameter vector, significantly enhancing computational efficiency. Finally, a posterior calibration module is introduced to compensate for the systematic biases caused by dimensionality reduction, strictly preserving the interpretability of the core inference architecture. To validate the effectiveness of the proposed framework, experimental evaluations are conducted on petroleum pipeline networks and liquid propellant launch vehicles. In the petroleum pipeline scenario, the rule base scale is reduced by over 60 percent from 56 to approximately 20 rules, while the parameter dimensionality decreases from 338 to 122. Compared to the conventional model, the mean squared error is reduced from 0.5291 to 0.3619. Furthermore, in the liquid propellant launch vehicle case, the model achieves a prediction accuracy of 98.57 percent with a mean squared error of 0.00029 while reducing the rule scale from 441 to 109. These results demonstrate that the BRB-ARR model effectively balances structural compactness with high precision prediction, offering a novel approach to uncertainty modeling in intelligent systems.
Journal Article
Domain-Driven Teacher–Student Machine Learning Framework for Predicting Slope Stability Under Dry Conditions
Slope stability prediction is a critical task in geotechnical engineering, but machine learning (ML) models require large datasets, which are often costly and time-consuming to obtain. This study proposes a domain-driven teacher–student framework to overcome data limitations for predicting the dry factor of safety (FS dry). The teacher model, XGBoost, was trained on the original dataset to capture nonlinear relationships among key site-specific features (unit weight, cohesion, friction angle) and assign pseudo-labels to synthetic samples generated via domain-driven simulations. Six student models, random forest (RF), decision tree (DT), shallow artificial neural network (SNN), linear regression (LR), support vector regression (SVR), and K-nearest neighbors (KNN), were trained on the augmented dataset to approximate the teacher’s predictions. Models were evaluated using a train–test split and five-fold cross-validation. RF achieved the highest predictive accuracy, with an R2 of up to 0.9663 and low error metrics (MAE = 0.0233, RMSE = 0.0531), outperforming other student models. Integrating domain knowledge and synthetic data improved prediction reliability despite limited experimental datasets. The framework provides a robust and interpretable tool for slope stability assessment, supporting infrastructure safety in regions with sparse geotechnical data. Future work will expand the dataset with additional field and laboratory tests to further improve model performance.
Journal Article
Forecast of Fire Protection Situation in High-Rise Buildings Based on Multi-Sensor Information Fusion
With the continuous development of modern science and technology and the increase in the amount of multi-sensor information, the prediction of the fire protection situation of high-rise buildings has gradually become a challenge. This paper establishes a prediction system for the fire protection situation of high-rise buildings and uses multi-sensor information fusion technology for high-rise buildings. The building fire protection situation index weight is weighted, and the original sensors are optimized to verify the prediction effect of the high-rise building fire protection situation. The experimental analysis results show that this method can reduce the initiative of the weight of the fire protection index of high-rise buildings and improve the accuracy of prediction.
Journal Article
Safety biomarkers for development of vaccines and biologics: Report from the safety biomarkers symposium held on November 28–29, 2017, Marcy l’Etoile, France
Vaccines prevent infectious diseases, but vaccination is not without risk and adverse events are reported although they are more commonly reported for biologicals than for vaccines. Vaccines and biologicals must undergo vigorous assessment before and after licensure to minimise safety concerns. Potential safety concerns should be identified as early as possible during the development for vaccines and biologicals to minimize investment risk. State-of-the art tools and methods to identify safety concerns and biomarkers that are predictive of clinical outcomes are indispensable. For vaccines and adjuvant formulations, systems biology approaches, supported by single-cell microfluidics applied to translational studies between preclinical and clinical studies, could improve reactogenicity and safety predictions. Next-generation animal models for clinical assessment of injection-site reactions with greater relevance for target human population and criteria to define the level of acceptability of local reactogenicity at vaccine injection sites in pre-clinical animal species should be assessed. Advanced in silico machine-learning-based analytics, species-specific cell or tissue expression, receptor occupancy and kinetics and cell-based assays for functional activity are needed to improve pre-clinical safety assessment of biologicals. The in vitro MIMIC® system could be used to compliment preclinical and clinical studies for assessing immune-toxicity, immunogenicity, immuno-inflammatory and mode of action of biologicals and vaccines. Sanofi Pasteur brought together leading experts in this field to review the state-of-the-art at a unique ‘Safety Biomarkers Symposium’ on 28–29 November 2017. Here we summarise the proceedings of this symposium. This unique scientific meeting confirmed the importance for institutions and industrial organizations to collaborate to develop tools and methods needed for predicting reactogenicity and immune-inflammatory reactions to vaccines and biologicals, and to develop more accuracy, reliability safety biomarkers, to inform decisions on the attrition or advancement of vaccines and biologicals.
Journal Article