Bo Wang

Large-scale hydroelectric projects play an essential role in sustainable energy infrastructure, yet their construction schedules are frequently disrupted by complex risk correlations and propagation effects. Traditional critical chain buffer management methods often rely on static presets, impeding dynamic allocation. This paper proposes a dynamic monitoring framework for critical chain buffers that enhances schedule resilience by accounting for risk interdependencies. The method employs a fuzzy DEMATEL-ISM-BN algorithm to identify risk propagation paths and quantify their impact on buffers; combines a WBS-RBS mapping matrix with fuzzy expert scoring to determine activity risk weights; uses a PSO-RF model to predict the actual activity completion times; and defines three buffer consumption scenarios based on the predictions to trigger dynamic reallocation. The approach is demonstrated through a case study of a pumped-storage power station in East China. The calculated project buffer was increased from 19.51 days under the root variance approach to 30.56 days after incorporating risk compensation, representing an increase of 56.6% and demonstrating the additional protection required when risk interdependencies are considered in buffer sizing. When critical activities encounter unexpected risks, the dynamic reallocation mechanism promptly redistributes buffers to compensate, preventing delays from propagating downstream along the critical chain. Under the case-study scenario, the total remaining buffer at project completion was 101.09 days. After accounting for the risk-induced extension of activity M, the resulting critical chain duration was 1940.05 days, which was 97.50 days (approximately 4.79%) shorter than the planned critical chain schedule of 2037.55 days. The proposed mechanism enables unused buffer from completed activities to be transferred to subsequent activities, thereby improving buffer utilization efficiency during project execution. Through a closed-loop mechanism of “risk quantification → buffer calculation → schedule forecasting → dynamic allocation,” the proposed framework transforms schedule management from static presetting to adaptive control, improves project resilience and resource utilization efficiency, and supports sustainable project delivery by improving schedule reliability and reducing unnecessary buffer allocation.

In-transit vehicle malfunctions during material transportation in water conservancy construction can cause delivery delays, disrupt subsequent tasks, and lead to duplicate material requisitions. To address these issues, this study develops a material transportation scheduling optimization model that explicitly incorporates vehicle-malfunction response. The model minimizes total transportation cost subject to vehicle capacity, resource compatibility, time-window, and non-duplicate-requisition constraints. Two response strategies are considered: continued transportation after on-site repair and relay transportation using an external temporary emergency vehicle. The failed task and its affected subsequent tasks are dynamically rescheduled. An adaptive hill-climbing genetic algorithm (AHCGA) is employed to coordinate transportation-batch generation, task sequencing, and resource assignment. The case study shows that, under the no-malfunction baseline scenario, all transportation batches complete unloading within their acceptable time windows. Across 30 runs, AHCGA achieves a mean total cost of CNY 9179.86; Wilcoxon signed-rank tests indicate statistically significant cost differences between AHCGA and both GA and HCGA (p < 0.001). The best AHCGA run yields a total transportation cost of CNY 8794.40 with zero total delay. Multi-scenario comparisons and sensitivity analyses further show that neither on-site repair nor external relay is universally dominant. Their relative suitability depends jointly on failed-task characteristics, on-site response and repair time, emergency-vehicle response time, transshipment efficiency, and call-out cost. The proposed model provides quantitative decision support for material transportation organization, vehicle-malfunction response, and post-malfunction rescheduling in water conservancy construction.

To address the challenges of relying on experience for vehicle allocation in concrete transportation for dam construction—as well as the difficulty in coordinating vehicle capacity, loading/unloading capabilities, and system queuing status—this study focuses on a closed-loop transportation system comprising “batching plant—transport route—pouring area” and develops a vehicle allocation model based on a BCMP (Baskett–Chandy–Muntz–Palacios) closed-loop queuing network. The loading process at the mixing plant and the unloading process at the silo yard are modeled as finite-service nodes, while the transportation of loaded vehicles and the return of empty vehicles are modeled as infinite-service nodes. The Buzen convolution algorithm is used to solve for the system’s steady-state performance. With the number of vehicles N and the silo yard diversion ratio p as joint decision variables, the feasible region for vehicle configuration and recommended solutions are determined subject to constraints on silo yard feed demand and node utilization. The results of the case study show that, using reference operating condition S0 as a benchmark, a decrease in the loading efficiency of the batching plant increased the recommended number of vehicles by 28.57%; a simultaneous increase in the unloading efficiency of the dual-bin system reduced the recommended number of vehicles by 42.86%; enhanced coordination between the loading and unloading systems reduced the recommended number of vehicles by 50.00% and decreased the expected queuing time per cycle by 97.26%; furthermore, the system bottleneck shifted as loading and unloading capacities changed. Further independent validation was conducted using discrete-event simulation (DES); the maximum relative error between BCMP and DES in terms of system throughput, expected queuing wait time per cycle, and maximum node utilization was less than 1.5%. Sensitivity analysis indicates that concrete pouring demand and unloading capacity at the bin area are key factors affecting vehicle allocation. When demand increases or unloading capacity decreases beyond the system’s capacity limits, simply adding more vehicles does not result in a feasible solution. The study demonstrates that the proposed method can quantitatively reveal the relationships among vehicle fleet size, traffic diversion at the bin area, loading and unloading capacity, and queueing conditions, thereby providing a decision-making basis for the coordinated allocation of vehicles and loading/unloading resources in concrete construction for dams.

Water resources are essential for sustainable agricultural development, and accurate crop water requirement prediction is important for improving irrigation efficiency and optimizing water allocation in irrigation districts. This study focused on the Chushandian Irrigation District in Henan Province, China. Reference evapotranspiration (ET₀) was calculated using the FAO Penman–Monteith equation, and the monthly crop water requirements (ET_C) of wheat, peanut, rapeseed, corn, rice, and vegetables were estimated using crop coefficients (K_c). XGBoost feature importance, Pearson correlation, Mantel, and SHAP analyses were used to examine the meteorological drivers of crop water requirement. Atmospheric pressure showed high nonlinear predictive importance, whereas mean air temperature, relative humidity, and sunshine duration exhibited more consistent physical and statistical relationships with crop water requirement. A process-informed TCN–Transformer framework was then developed for joint and crop-specific prediction. The TCN module extracted local temporal variations, while the Transformer module captured long-term dependencies. In the joint prediction task, the proposed model achieved an R² of 0.9487 and an RMSE of 33.24 mm, outperforming the LSTM, GRU, and CNN–LSTM baselines. The crop-specific results further demonstrated that the model effectively represented seasonal variations and periods of relatively high water requirement across the six crops. The proposed framework can support monthly water-allocation planning and seasonal irrigation scheduling in multi-cropping irrigation districts.

Inter-basin water transfer projects (IBWTPs) play a pivotal role in alleviating the spatiotemporal imbalances of water resources. However, their operation is exposed to multiple, highly interdependent safety risks that can significantly undermine system stability and water supply reliability. Existing studies predominantly focus on isolated risk factors or rely heavily on subjective data, which limits their ability to capture the complex interrelationships among risks and reveal their underlying propagation mechanisms. To address these limitations, this study proposes a novel risk correlation analysis framework that integrates Interpretive Structural Modeling (ISM) with copula functions. ISM is first employed as a preprocessing tool to structure expert knowledge and develop an initial risk correlation framework. It is then used to hierarchically organize the complex interrelationships among risks. Subsequently, copula functions are utilized to model nonlinear dependencies and tail behaviors among risk variables. This enables a quantitative assessment of correlation strengths and facilitates the construction of a risk topological network. An empirical case study is conducted based on the Middle Route of the South-to-North Water Diversion Project. The results reveal 13 significant correlations among six second-level risk categories. Natural risks (e.g., floods and geological hazards) are identified as the primary driving factors. They exhibit a strong positive correlation (0.6155) with engineering risks and serve as the most critical nodes for proactive risk prevention and control. Engineering risks function as central intermediary hubs in the risk transmission process, whereas water quality and economic risks are characterized as terminal endpoints. Furthermore, three principal risk propagation pathways are identified: (1) natural risks → engineering risks → economic risks; (2) natural risks → operational scheduling risks → social risks; and (3) engineering risks → water quality risks → economic risks. The resulting risk topological network demonstrates significant small-world properties, indicating highly efficient risk transmission within the system. Ultimately, this study provides a robust quantitative approach for analyzing risk interactions in complex engineering systems and enriches the theoretical framework of engineering risk management. It also identifies critical nodes and key transmission pathways for risk prevention and control in IBWTPs, thereby offering significant practical implications for operational safety.

Inter-basin water transfer projects (IBWTPs) play a crucial role in addressing the uneven spatial and temporal distribution of water resources and ensuring water security in the receiving areas. However, these projects are subject to various risk factors during their operation. While risk management is critical, current research in this field lacks a systematic and dynamic approach. A three-dimensional measurement model for probability, loss, and risk value, based on Dempster–Shafer (DS) evidence theory, Bayesian networks, and the equivalence method, was established in this study and, in consideration of the engineering characteristics of the IBWTP, a dynamic transmission evolution model for risk is constructed. The applicability and effectiveness of the model are demonstrated through a case study of the Central Line Project of South-to-North Water Diversion (CLPSNWD). The results indicate that the system risk of the CLPSNWD is in an unstable state, with the key influencing factors being channel engineering risk, flood disaster risk, pipeline engineering risk, and water transfer (discharge) cross-structure risk. The research findings offer a novel approach to the quantitative analysis and evolution of risk and contribute to the further development of engineering risk management theory.

The composition elements of the earthwork allocation system (excavation project, filling project, transfer yard, waste disposal yard, and material yard) and the relationship between material flow were analyzed. Based on the construction of calculation models for soil and water conservation fees, landscape restoration fees, and road transportation intensity, a joint optimization model was constructed with the objectives of minimizing the total allocation cost and minimizing the peak transportation intensity of the road. By dynamically adjusting the volatility, setting penalty factors, and vectorizing NumPy arrays, the ant colony algorithm is improved and the optimization model is solved. Case analysis shows that considering the intensity of road transportation, the peak transportation intensity significantly decreases, and the proportion of directly filled earthwork increases to over 88% without exceeding the capacity of the intermediate transfer site. The total cost only increases by 0.91%, and the allocation plan is more in line with actual construction needs.

As a critical component of China’s major infrastructure, the quality and safety of hydraulic engineering projects are directly linked to national economic security. Therefore, research on construction quality management of hydraulic concrete is of great importance. Traditional quality gain–loss functions often fail to fully capture the correlations among multiple quality characteristics, the varying weights of these characteristics in overall quality performance, and the presence of multiple influencing factors. To address these limitations, this study employs Gaussian process regression to construct a multivariate and multidimensional quality gain–loss function model. The signal-to-noise ratio is used to represent the interactions among different quality characteristics, while a gain–loss cost matrix is introduced to account for the contribution of each characteristic to the overall function. A case study on summer dam concrete construction is presented to demonstrate the applicability of the proposed model. The results show that the gain–loss values range from a minimum of 1.09 to a maximum of 11.7, which are significantly lower than those obtained using the dimensionless standardized multivariate quality gain–loss function developed by Artiles-León, thereby validating the effectiveness and rationality of the proposed approach.

In response to the difficulty of quantifying the fuzziness and randomness of safety risks in highway engineering construction using traditional methods, which leads to insufficient reliability, this study proposes a risk assessment method for highway engineering construction safety reliability based on the two-dimensional cloud model and Bayesian network. An assessment system containing 4 categories and 18 indicators was constructed using the Delphi method, a fault tree model was established and transformed into a Bayesian network. Based on the risk matrix method, a two-dimensional cloud model was constructed and the comprehensive cloud of bottom events was calculated. By assessing the occurrence probability of the bottom events and the severity of their consequences, the a priori probabilities of the risk indicators were calculated. Bidirectional inference in the Bayesian network was then used to determine the probability of the top event and identify key factors.

Based on the Copula theory, a multiple correlation analysis model for the operation safety risks of long-distance water diversion projects was established. Combined with Bayesian network reasoning, a polynomial regression analysis, and other techniques, a dynamic analysis method for the operation safety of long-distance water diversion projects based on a Copula Bayesian network model was proposed, providing decision support for the operation safety risk management of long-distance water diversion projects. We took the Middle Route Project of the South-to-North Water Diversion Project as an example to verify the validity and practicability of the model. The results show that this method can capture the nonlinear mapping relationship when the probability of risk occurrence changes dynamically on the basis of considering the risk correlation, and realize the dynamic analysis of risk correlation.