The deformation performance of aluminum alloy formwork under stress is investigated in this research using static tests. The entire model of aluminum alloy formwork under load during construction is investigated using finite element simulation. And then, there’s the project’s actual value. The function is established during the construction period under the limit state of typical use of the wall in terms of reliability. The fluctuation characteristics of the dependability index of the out-of-plane deformation of the concrete wall are explored, and various results are produced, by taking into account the various reinforcement variables of the aluminum formwork. The study discovered that when the number of uses increases during the turnover usage process, the bearing capacity of the building formwork support gradually decreases, and the associated reliability index falls below 2. Adding horizontal and vertical rods above 3.6 m to the single support system, in combination with other elements, is recommended to strengthen its stability. The results reveal that joint semirigidity and wall thickness have a significant influence on the ultimate bearing capacity of construction formwork. The change characteristics of building formwork ultimate bearing capacity in the process of repeated turnover use are studied by increasing the initial defect method to simulate the increase of building formwork turnover times, with the conclusion that the more repeated use times, the lower the building formwork ultimate bearing capacity.

Deep excavations are high-risk geotechnical activities, and accurate prediction of diaphragm-wall settlement is important for construction monitoring and deformation control. This study investigates cumulative vertical settlement at 23 diaphragm-wall monitoring points from the deep excavation of Tianjin Goldin Finance 117 in Tianjin, China. Seven prediction models—a naïve persistence model, autoregressive integrated moving average (ARIMA), K-nearest neighbors (KNN), multilayer perceptron (MLP), gated recurrent unit (GRU), Transformer, and XGBoost—were evaluated using a unified five-fold rolling-origin expanding-window validation scheme. GRU achieved the best overall baseline performance, with a mean R² of 0.9172, a mean absolute error (MAE) of 0.1009 mm, a root mean square error (RMSE) of 0.1341 mm, and a mean absolute percentage error (MAPE) of 0.5875%. GRU was subsequently optimized using the crow search algorithm (CSA), the genetic algorithm (GA), and the whale optimization algorithm (WOA). GRU-WOA achieved the best numerical performance, with a mean R² of 0.9289 and an RMSE of 0.1215 mm. Relative anomaly levels were further identified from predicted settlement-change rates to characterize temporal concentration and spatial clustering of settlement-change activity. The proposed framework can support priority inspection and targeted monitoring, although the resulting anomaly levels represent project-relative statistical deviations rather than code-based engineering risk classes.

Mass concrete design presents a significant challenge due to the inherent conflicts among key performance metrics: high compressive strength, low heat of hydration, and low water absorption (a key durability indicator). Traditional trial-and-error methods are inefficient and fail to systematically navigate these complex trade-offs. To address this, this study proposes a data-driven multi-objective optimization framework for mass concrete mix design. A comprehensive experimental dataset of 64 mixtures was established by varying the water-to-binder ratio (0.40–0.55), fly ash content (0–120 kg/m³), and slag content (0–120 kg/m³), with cement content fixed at 400 kg/m³. Kriging surrogate models were developed to accurately map the nonlinear relationships between these design variables and the three performance responses. These models were then integrated with the NSGA-II algorithm to generate a Pareto-optimal front of solutions. The framework’s predictive accuracy and generalization capability were rigorously validated through out-of-sample experiments, demonstrating prediction errors consistently below 10%. The results provide a quantified map of feasible engineering compromises, enabling engineers to select tailored mixtures for specific project priorities, such as low-heat mixes for dams or high-strength mixes for foundations.

The long‐term degradation of damper performance under adverse conditions compromises energy dissipation, posing a potential risk to structural safety. Online degradation identification approaches provide benefits, including in situ evaluation, real‐time processing, and high computational efficiency, among which Kalman filter–based methods offer unbiased minimum variance estimates from partial measurements. However, parameter identification in multidamper systems remains challenging, primarily due to the ill‐posed nature of estimating numerous unknowns from limited measurements. To overcome this limitation, this paper proposes a novel sparse extended Kalman filter under unknown input (SEKF‐UI) algorithm. In this method, damper damage factors are incorporated into an extended state vector while explicitly leveraging their sparsity as prior information. The sparsity constraint is seamlessly embedded within the recursive steps via the pseudomeasurement (PM) technique, thereby avoiding additional offline optimization and preserving the real‐time advantage. Thus, the proposed approach effectively addresses the ill‐posed inverse problem, enabling the simultaneous identification of multidamper damage factors and unknown inputs. Moreover, it preserves the recursive framework of the classical Kalman filter while significantly enhancing its robustness and accuracy. Numerical validation employs an 8‐story shear frame, a single‐span truss, and a 3‐story benchmark model, each equipped with a different damper mechanical model. Results confirm that the proposed algorithm outperforms conventional methods in identifying parameter degradation across multidamper systems.