The accurate prediction of concrete temperature during arch dam construction is essential for crack prevention. The internal temperature of the poured blocks is influenced by dynamic factors such as material properties, age, heat dissipation conditions, and temperature control measures, which are highly time-varying. Conventional temperature prediction models, which rely on offline data training, struggle to capture these time-varying dynamics, resulting in insufficient prediction accuracy. To overcome these limitations, this study constructed a sparrow search algorithm–incremental support vector regression (SSA-ISVR) model for online concrete temperature prediction. First, the SSA was employed to optimize the penalty and kernel coefficients of the ISVR algorithm, minimizing errors between predicted and measured temperatures to establish a pretrained initial temperature prediction model. Second, untrained samples were dynamically monitored and incorporated using the Karush–Kuhn–Tucker (KKT) conditions to identify unlearned information, prompting model updates. Additionally, redundant samples were removed based on sample similarity and error-driven criteria to enhance training efficiency. Finally, the model’s accuracy and reliability were validated through actual case studies and compared to the LSTM, BP, and ISVR models. The results indicate that the SSA-ISVR model outperforms the aforementioned models, effectively capturing the temperature changes and accurately predicting the variations, with a mean absolute error of 0.14 °C.

To better understand the temperature changes in face slab concrete and address challenges such as delayed curing and outdated methods in complex and variable environments, this study investigates the use of visualization and real-time feedback control in concrete construction. The conducted study systematically develops an intelligent curing control system for face slab concrete based on multi-source measured data. A tailored multi-source data acquisition scheme was proposed, supported by an IoT-based transmission framework. Cloud-based data analysis and feedback control mechanisms were implemented, along with a decoupled front-end and back-end system platform. This platform integrates essential functions such as two-way communication with gateway devices, data processing and analysis, system visualization, and intelligent curing control. In conjunction with the ongoing Maerdang concrete face rockfill dam (CFRD) project, located in a high-altitude, cold-climate region, an intelligent curing system platform for face slab concrete was developed. The platform enables three core visualization functions: (1) monitoring the pouring progress of face slab concrete, (2) the early warning and prediction of temperature exceedance, and (3) dynamic feedback and adjustment of curing measures. The research outcomes were successfully applied to the intelligent curing of the Maerdang face slab concrete, providing both theoretical insight and practical support for achieving scientific and precise curing control.

The cable crane is the core hoisting equipment for high arch dam construction, and its hoisting trajectory is critical for both operational efficiency and safety. However, current trajectory planning does not adequately consider the underactuated characteristics of the cable crane. For instance, sudden stops or abrupt changes in direction can easily induce large swings of the bucket, causing safety risks and equipment wear. To address this issue, this paper developed a trajectory planning model for obstacle avoidance with smooth transitions in cable crane hoisting for arch dams and solved the high-dimensional optimization problem using a path–velocity decoupling strategy. First, a shortest path with geometrical conciseness and free collision was generated based on an improved A* algorithm to reduce the frequency of directional changes. Next, for different hoisting scenarios, segmented S-curve and polynomial velocity functions were proposed to ensure smooth velocity transitions. Then, an orthogonal experimental design was employed to generate a cluster of candidate trajectories that meet kinematic constraints, from which the optimal trajectory was selected using a multi-objective evaluation function. The results demonstrate that the motion trajectory planned using the proposed method is notably smoother. Compared with the traditional trapezoidal velocity method, it reduces the maximum swing amplitude of the bucket by 40.78% at a modest time cost. In real-time obstacle avoidance scenarios, the approach outperforms emergency-stop strategies, reducing the bucket’s maximum swing amplitude by 30.48%. This work will provide a reference for engineers to optimize the trajectory of large lifting equipment in construction fields such as high arch dams and bridges.

The quality of concrete consolidation is critical to the structural safety of dams. However, current vibration machines lack reliable information regarding the distribution map of concrete vibration states in a pouring block. This informational deficiency often causes machinery to repeatedly traverse previously vibrated regions, which compromises the internal structural integrity of the concrete. Accurately recognizing these vibrated regions is challenging because vibration-induced particle rearrangement and concrete flow-ability lead to high inter-class similarity, minimal color variance, and blurred boundaries in practical construction environments. To address this issue, this study proposes a recognition method utilizing a Transformer architecture with masked attention. This design enhances feature representation, effectively focusing on target regions and ambiguous boundaries while suppressing background noise. Experimental results demonstrate the robustness of the proposed method, achieving a mean Average Precision of approximately 0.9. This performance outperforms existing benchmark models, exceeding YOLACT, YOLO11, CondInst, and SOLOv2 by roughly 97%, 53%, 17%, and 12%, respectively. Practically, embedding concrete state distribution information into machinery provides visual guidance for path planning, enabling a lean control from data acquisition to physical execution. Future research will integrate these findings with construction equipment to enable autonomous operation and decision-making for intelligent quality control.

In the construction of conventional concrete high arch dams in high-altitude regions with large temperature variations, the prolonged and cold winters often force the suspension of concrete pouring, severely constraining the overall schedule. To address this limitation, this paper breaks away from the conventional winter-shutdown scheme by proposing a new technique: continuous construction under low-temperature conditions. It can adapt to large temperature variations, and this study develops a corresponding construction schedule simulation model for quantitative evaluation and scheme optimization. First, the influence of large diurnal temperature variations on high-altitude concrete pouring was analyzed. Based on this, a dynamic pouring technique for sub-blocks is proposed—thin-layer pouring during positive temperatures and insulation curing during negative temperatures—with the aim of transforming discrete climatic windows into a continuous construction period. Second, to accurately simulate this complex spatial partitioning and temporal scheduling process, a customized schedule simulation model based on discrete-event simulation (DES) theory was developed. The model incorporated meteorological recognition at low temperatures, dynamic dam-block partitioning, and sub-block pouring scheduling. Finally, a high arch dam on a plateau in Southwest China was used as an engineering case to compare two construction schemes: the low-temperature shutdown scheme and the continuous construction scheme. After validating the simulation model under parameter assumptions such as ideal resource availability and stable annual climate patterns, the results showed that the continuous construction scheme achieves a monthly average pouring volume of 33,721 m³ during the period with large diurnal temperature variations, which accounts for 42.48% of the average monthly pouring volume during the normal construction period. Compared to the low-temperature shutdown scheme, the coefficient of variation of the monthly pouring intensity decreases by about 40%, and the total construction period is shortened by approximately ten months. This demonstrates the potential for schedule optimization for continuous winter construction in simulation.