Safety Engineering Research Articles

Human Skin Burn Intensity Resulting From Various Incidents Utilizing Bioheat Transfer Model: A Comparative Analogy

ABSTRACTUnderstanding how human skin reacts to heat is vital for effective burn prevention and treatment. This study uses a bioheat transfer model to develop a comparative analogy to differentiate burn intensity from hot dishes, hot fluids, radiation, and flash fires, aiming to differentiate the burn profiles of each incident type. The finite element method is employed to solve the time‐dependent Pennes' bioheat transport equation concerning the three distinct layers of human skin. The Arrhenius equation is implemented to quantify the damage fraction associated with thermal burns. The burn intensities for the degrees of burns (first, second, and third) are evaluated by applying Henriques burn integral, considering various burning conditions and the corresponding exposure times required for each burn. The numerical results are displayed in various formats, including volume temperature plots and line graphs of damage fraction. The findings reveal that first‐degree burns occur the fastest, followed by second‐degree burns, with third‐degree burns taking the longest to develop. Notably, burns from direct contact with a hot dish are more severe than those from freely flowing heated fluids. The results highlight that exposure time, temperature, and thermal conductivity are key factors in burn depth and tissue damage, offering valuable insights for burn treatment and risk management. The outcomes will effectively predict burn consequences, making it useful in burn injury treatment and safety engineering.

A method for monitoring three dimensional surface deformation in mining areas combining SBAS-InSAR, GNSS and probability integral method

In the process of mineral resource extraction, monitoring surface deformation is crucial for ensuring the safety of engineering and ground infrastructure. Monitoring complete three-dimensional surface deformation is particularly significant. Traditional synthetic aperture radar (InSAR) technology provides deformation components only along the line of sight (LOS) and often lacks sufficient effective data in vegetation-covered mining areas and mining subsidence centers. To address this, this study proposes a method (SBAS-PIM) that combines SBAS-InSAR with the probabilistic integral method (PIM). This method leverages high-coherence points in mining areas and GNSS data from vegetation-covered regions to invert the parameters required by PIM, thus obtaining three-dimensional surface deformation results. The proposed method allows for the acquisition of three-dimensional deformation data with fewer InSAR points and GNSS data, significantly reducing labor costs and addressing the gap in InSAR monitoring of three-dimensional surface deformation in densely vegetated areas. Additionally, it accounts for the mutual influence of multiple adjacent working faces. Finally, through the application to a mining area in Heze, China, the maximum displacements in the vertical, east–west, and north–south directions were obtained as −2011, −418, and − 281 mm, respectively. The correlation coefficients between the vertical and east–west directions and GNSS data were both greater than or equal to 0.9, indicating that this method can effectively monitor the three-dimensional surface deformation of the mining area.

Innovative Practices in Large-span Prestressed Concrete Beam Construction for Housing Projects

Large-span prestressed concrete beam is a structural form widely used in modern buildings, and the rationality of its construction technology is directly related to the engineering quality and safety. Article on the analysis of large span prestressed concrete beam construction points, on the basis of new template support system application, steel connection, high performance concrete mix ratio design, intelligent tension system application of key construction technology, and discusses the BIM technology application, prefabricated construction, green construction process optimization and innovation. The results show that the construction technology of large-span prestressed concrete beam is feasible and effective, which can provide a reference for similar projects.

Assessment of Peak Particle Velocity of Blast Vibration using Hybrid Soft Computing Approaches

Abstract Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-CatBoost model that integrates variational mode decomposition (VMD) and the CatBoost algorithm, with hyperparameters globally optimized using the improved hippocampus optimization algorithm (IHO). Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model's interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.

Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction method is developed to model joints with arbitrary shape characteristics. The numerical model is calibrated through 3D printing and direct shear tests. Systematic parameter analysis validates the selected quantitative indices as effective descriptors of joint morphology. Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. Machine learning, recognized as a rapidly advancing field, plays a pivotal role in data-driven engineering applications due to its powerful analytical capabilities. In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. The performance of each algorithm is assessed through comparative analysis of their predictive accuracy based on correlation coefficients. The results demonstrate that all six algorithms achieve satisfactory predictive performance. Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets.

Disintegration Mechanism of Coal-Bearing Soil Based on Granularity Entropy and Water-Air Two-Phase Flow.

Coal-bearing soils (CBS), products of coal-bearing strata weathering, are particularly prone to disintegration due to the effects of dry-wet cycles. Static water disintegration tests, environmental scanning electron microscopy (ESEM), and mineral chemical composition analyses were conducted on CBS. The disintegration evolution of CBS is characterized by granularity entropy and is analyzed concerning the disintegration ratio. Furthermore, the disintegration mechanism is examined based on the water-air two-phase flow (WTF) and mineral chemical reactions. Results show a significant exponential relationship between the standard basic entropy (A) and disintegration ratio (DR), where the disintegration ratio decreases as the standard basic entropy increases. As the number of dry-wet cycles increases, A initially decreases rapidly before stabilizing, mirroring the variation pattern of the particle size distribution curve and its derived indicators. Illite produces significant short-range hydration repulsion, leading to the formation of additional cracks in CBS. WTF significantly influences disintegration; water intrusion increases air pressure, and the subsequent pressure release plays a critical role in damaging soil structure. These findings are significant for the safety and protection of CBS slope engineering.

Preventive maintenance on highway roads

Dangerous geological processes such as landslides, mudflows, rockfalls, and bank erosion are observed in many regions of Russia. This paper aims to expand the classification of works on engineering protection of highway sections from hazardous geological processes to reduce the risk of emergencies. The issues of engineering protection of highways are considered, as well as the principles (conditions) formed in the process of their construction, which provide for timely organizational and constructive decisions at the stages of design and subsequent operation. According to the Decree of the Ministry of Transport of the Russian Federation No. 402 dated November 16, 2012, the following classification of works was approved: overhaul, repair of highways, maintenance works of highways. Not only designing new means of engineering protection of highways, but also maintaining the proper functioning of existing ones is relevant for specialists in the field of road construction. A list of recommended measures is designed to ensure reliable and fail-safe operation of retaining walls as well as to analyze the technical condition of landslide protection structures for subsequent measures to prevent emergencies. The present paper indicates a new type of work, i.e. preventive maintenance works, which may involve minor modifications to the retaining wall structure to strengthen it. When they are performed, overhaul of the site is not required.

Final thoughts: The fragile connection of safety and science in the geological disposal of radioactive waste

ABSTRACT While research on the safety of geological disposal of radioactive waste has been ongoing for more than 50 years, the research community has not achieved a clear understanding of repository safety. Repository safety is often considered a concept developed by safety engineers, putting ever-growing knowledge in the overall context of a safety case, with scientists providing building blocks for it, some of which might not contribute to safety. This article argues that scientists need to increase their overall understanding of repository safety and in particular of the redundancy of various barriers assuring the isolation capacity of a repository over an unprecedented long time.

Engineering safety in the aspect of the safety and security civilization

Engineering safety in the aspect of the safety and security civilization

Prof. James G. Quintiere, a legend in fire safety science and engineering, dies at age 84

Prof. James G. Quintiere, a legend in fire safety science and engineering, dies at age 84

Addressing the Importance of STEM to Public Science Communication in Engineering Formation: An Intervention Analysis for an Upper-level Chemical Engineering Health and Safety Course

Addressing the Importance of STEM to Public Science Communication in Engineering Formation: An Intervention Analysis for an Upper-level Chemical Engineering Health and Safety Course

Optimizing ergonomic work facilities in distribution logistics to prevent manual lifting injuries

This study aims to improve ergonomic conditions for box-lifting operators in bottled drinking water companies. Operators handling small packaging sizes (600 ml) report significant pain, as revealed by the Nordic Body Map Questionnaire. Addressing this issue is crucial for preventing manual lifting injuries and ensuring worker safety in logistic distribution. A combination of biomechanical analysis, anthropometric data, and the Nordic Body Map Questionnaire was used to assess operator complaints and evaluate ergonomic hazards. The study utilized Catia software to simulate work facility designs, focusing on the development of an adjustable-height hydraulic pallet to optimize operator posture during lifting tasks. Key metrics included compressive and shear force calculations to evaluate injury risks. Operators reported pain in the shoulders, lower back, buttocks, and thighs over the past year. Initial evaluations showed excessive compressive forces (up to 19,778.2 N) and shear forces (over 500 N), indicating a high risk of injury. After ergonomic interventions, simulations recorded compressive forces of 3,350 N and shear forces of 185.31 N, demonstrating a significant reduction in risk and safe operational conditions. This study offers a novel, comprehensive approach to ergonomic optimization in logistics, combining operator feedback, biomechanical analysis, and technological tools like Catia for facility design. The findings provide a blueprint for improving worker safety and efficiency in manual lifting tasks. The study’s outcomes benefit safety engineers, ergonomic specialists, and logistics managers, offering insights into improving worker well-being and operational efficiency. Future research could explore further technological enhancements in facility design and their impact on worker ergonomics.

Revolutionising Cancer Immunotherapy: Advancements and Prospects in Non-Viral CAR-NK Cell Engineering.

The recent advancements in cancer immunotherapy have spotlighted the potential of natural killer (NK) cells, particularly chimeric antigen receptor (CAR)-transduced NK cells. These cells, pivotal in innate immunity, offer a rapid and potent response against cancer cells and pathogens without the need for prior sensitization or recognition of peptide antigens. Although NK cell genetic modification is evolving, the viral transduction method continues to be inefficient and fraught with risks, often resulting in cytotoxic outcomes and the possibility of insertional mutagenesis. Consequently, there has been a surge in the development of non-viral transfection technologies to overcome these challenges in NK cell engineering. Non-viral approaches for CAR-NK cell generation are becoming increasingly essential. Cutting-edge techniques such as trogocytosis, electroporation, lipid nanoparticle (LNP) delivery, clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9) gene editing and transposons not only enhance the efficiency and safety of CAR-NK cell engineering but also open new avenues for novel therapeutic possibilities. Additionally, the infusion of technologies already successful in CAR T-cell therapy into the CAR-NK paradigm holds immense potential for further advancements. In this review, we present an overview of the potential of NK cells in cancer immunotherapies, as well as non-viral transfection technologies for engineering NK cells.

Numerical Simulation Study on Dominant Factors of Surge Hazards in Semi-Submerged Landslides

Landslide-generated surge waves are significant natural hazards, posing severe risks to engineering safety. Despite extensive research on the dynamics of landslide-generated waves, studies analyzing controlling factors and their mechanisms remain limited, leaving key influencing processes inadequately understood. This study utilizes computational fluid dynamics (CFD) to perform a numerical simulation of a semi-submerged landslide in a hydropower station reservoir area. The research systematically investigated the effects of key variables, including slide volume, velocity, centroid height, and water depth, on the behavior of semi-submerged landslide-generated surge waves. Results demonstrate a positive correlation of slide volume, velocity, and centroid height with the initial wave height and run-up on the opposing shoreline. However, the impact of water depth reveals a more complex pattern, exhibiting distinct surge characteristics in the near-field and far-field zones. Via correlation and sensitivity analyses, this study elucidated the relationships between these factors and surge dynamics, identifying the primary factors influencing the size of the semi-submerged landslide-generated surge. The findings provide critical insights for predicting and mitigating surge disasters, offering both theoretical foundations and practical application value for landslide disaster prevention and management.

Simulation study of fracturing pump connecting rod big end bearing wear failure based on multi-body dynamics

Fracturing pump is the core equipment in fracturing operations. Facing the process characteristics of shale gas fracturing operations, such as ultra-high pressure, large displacement, variable working conditions and long period, the key components of fracturing pump are prone to failure due to almost full load and non-intermittent operation. In this paper, a new type of five-cylinder fracturing pump is taken as the research object, and the vibration response of the fracturing pump under the corresponding failure modes is investigated. A rigid-flexible coupled dynamics simulation model of a new five-cylinder fracturing pump with typical failures of the key components is established by combining the multi-body system dynamics and the L-N nonlinear spring-damped contact force model. The vibration response of the pump is then investigated. The vibration response of the fracturing pump unit under no-load conditions with typical failures of the connecting rod and big end bearing is also investigated, as well as the data of the failure characteristics, which provide a theoretical basis for the understanding and prediction of the vibration behaviors of fracturing pumps under failure conditions. This study provides a theoretical basis for the fault diagnosis and health monitoring of fracturing pumps, enhancing their reliability and safety in practical engineering. Furthermore, it serves as an important reference for the fault diagnosis and prediction of reciprocating mechanical equipment.

Seabed Liquefaction Risk Assessment Based on Wave Spectrum Characteristics: A Case Study of the Yellow River Subaqueous Delta, China

Seabed liquefaction induced by wave loading poses considerable risks to marine structures and requires careful consideration in marine engineering design and construction. Traditional methods relying on statistical wave parameters for analyzing random waves often underestimate the potential for seabed liquefaction. To address this underestimation, the present study employs field observations and numerical simulations to examine wave characteristics and liquefaction distribution across various wave return periods in the Chengdao Sea area of the Yellow River subaqueous delta. The research results indicated that the wave decay phase exhibited a higher liquefaction potential than the growth phase, primarily because of the prevalence of low-frequency swell waves. The China Hydrological Code Spectrum (CHC Spectrum) effectively captured the wave characteristics in the study area, with parameterization grounded in measured data. The poro-elastic wave–sediment interaction model further elucidated the liquefaction distribution under extreme wave conditions, revealing a maximum liquefaction depth exceeding 3 m and prominent liquefaction zones at water depths of 5–15 m. Notably, seabed properties emerged as a critical factor for liquefaction and overshadowed water depth, with non-liquefaction zones occurring at water depths of less than 15 m at high clay content, highlighting the general liquefaction risk of silty seabed. This study enhances understanding of the seabed liquefaction process and offers valuable insights into engineering safety.

Summary of Research Status on Seismic Performance of Multi-tower Cable-stayed Bridge

As one of the important forms of modern bridge structure, the research on the seismic performance of multi-tower cable-stayed bridge is of great significance to the safety and durability of bridge engineering. In this paper, the research status of seismic performance of multi-tower cable-stayed bridges is reviewed from three aspects: experimental research, numerical analysis and theoretical research. The methods of improving seismic performance through structural optimization and the technical means of improving seismic performance through constraint system are discussed. The purpose of this paper is to summarize the existing research results, identify the existing problems, and propose that future research should focus on the application of new damping equipment and the discussion of hybrid control system, so as to provide reference for the seismic design of multi-tower cable-stayed bridge.

Study on the energy evolution process and damage constitutive model of concrete–granite composite specimens under uniaxial compression load

The interaction between concrete structures and rock foundations is a crucial research topic for assessing safety and stability in geotechnical and underground engineering. The uniaxial compression tests were conducted on different combination modes (concrete component heights (Hc), interface inclination angle (β), and coarse aggregate contents) to investigate their impact on the mechanical and energy response of concrete–granite composite specimens (CGCSs). This study categorized three failure modes: only concrete component failure (Hc = 80 mm), shear failure along the interface (β = 30°), and simultaneous failure of both components (other combination modes). The fractal dimension (Df) of surface cracks positively correlates with Hc, while the compressive strength (σCGCS) and stiffness (ECGCS) exhibit an inverse trend. The value of Df and σCGCS both exhibit a ''U-shaped'' trend when β ranges from 0° to 90°, whereas the value of ECGCS decreases linearly. Moreover, The value of Df and ECGCS positively correlate with coarse aggregate contents, while the value of σCGCS trends vary non-monotonically increases. The coarse aggregate contents have few effects on energy conversion. Typical brittle failure (β = 0°, β = 30°, and Hc = 20 mm) and ductile failure (other combination modes) are observed. Energy evolution characteristics offer quantitative insight into the damage evolution processes of CGCSs. The piecewise damage constitutive model based on dissipation energy can accurately describe the mechanical response of CGCSs. This study enhances understanding of the mechanical properties, failure characteristics, and energy evolution process of CGCSs under complex combination modes.

Investigation and application of data balancing and combined discriminant model in rock burst severity prediction

In the development of intelligent rock burst prediction models, issues such as incomplete data coverage and data imbalance are frequently encountered. These issues may lead to risks of overfitting in predictive models, poor generalization capabilities, and increased bias, which in turn may result in misjudgments and unpredictable losses. To accurately predict rock burst disasters and mitigate or eliminate related threats, this paper proposes a composite prediction model that integrates Density-Based Nonlinear Resampling (DBNR)-Tomek Link data balancing algorithms with Bayesian Optimization (BO)-Multilayer Perceptron (MLP)-Random Forest (RF). Initially, this study collected and organized a total of 301 recorded rock burst disaster field observation data, covering various tectonic plates, engineering types, rock origins, rock textures, and rock burst types. Subsequently, from a data analysis perspective, we employed the PCA-SSA-K-means unsupervised clustering algorithm to delve into the underlying information contained within the data, thereby validating the rationality of categorizing rock bursts into four grades. Then, using the L2 norm to optimize the dimensionality of the indicators and supplementing with indicator importance ranking and hypothesis testing, we selected the maximum tangential stress of the surrounding rock, the ratio of the maximum tangential stress of the surrounding rock to the uniaxial compressive strength of the rock (stress coefficient), and the elastic energy index as the criteria for rock burst intensity grading. Following that, the DBNR-Tomek Link sampling method was applied to balance the sample data, optimizing the data sample ratio and ultimately expanding the sample size to 396, improving the proportion of data samples from 2:3:4:1 to 1:1:2:1, thereby enhancing the model’s generalization performance. Ultimately, a BO-MLP-RF composite prediction model was constructed based on Bayesian Optimization (BO), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms, with the Bayesian Optimization method ensuring that the model fits the training data well and generalizes to the test data. The results of tenfold cross-validation demonstrated that the model’s accuracy is consistently around 92.5%, combining the training results of rock burst models with imbalanced datasets, proving that the MLP model, adept at modeling nonlinear data, and the RF model, skilled in modeling large-scale data, serve as basic classifiers. This demonstrates that the application of data balancing and combined discriminative model schemes have enhanced the model’s predictive performance and stability. The model is capable of providing high-accuracy, high-efficiency early warning monitoring services for rock burst phenomena in rock engineering, thereby ensuring engineering safety.

Engineering Safety and Ethical Challenges in 2045 Artificial Intelligence Singularity

Artificial intelligence (AI) has rapidly advanced, increasingly showcasing its powerful learning and computational capabilities. This progress has resulted in significant breakthroughs in areas such as image processing, speech recognition, and autonomous driving. Scientists predict that by around 2045, AI will overcome existing technological barriers, allowing strong AI to surpass human intelligence. However, it will inevitably affect human social relationships and order. Ethical issues associated with AI technology, such as unemployment, privacy breaches, and discrimination, generate a sense of threat among people, resulting in a loss of confidence in AI, which hampers its sustainable progress. Therefore, AI ethical issues are not only significant topics in academia but also become critical concerns for individuals, society, and nations. This article aims to address the challenges of AI ethics safety and the erosion of human confidence, while promoting the sustainable development of AI. It presents an AI ethics safety framework that analyzes engineering ethics and human trust within the context of sustainable AI development, and it recommends governance methods and strategies informed by case studies. Furthermore, we propose evaluation criteria and methods, establishing early-warning thresholds to keep potential AI risks within acceptable limits. Finally, the future prospects for AI ethics safety are highlighted. We hope our research contributes to the sustainable development of AI, ensuring that the arrival of the AI singularity has a positive impact on society with a long-term harmonious coexistence between AI and humanity.