Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.

The third-generation gravity base foundation, which consists of a concrete-based structure with infill aggregates, is designed for water depths greater than 20 m. In this study, a simplified method in the time domain for predicting the nonlinear dynamic response of the offshore wind turbine supported on the third-generation gravity base foundation is proposed. The results obtained by the proposed method are compared with 3D finite element simulations, and the consistency of the results verifies the reliability of the simplified method. In addition, the dynamic response of the wind turbine supported on GBF under wind and wave loads is investigated. The results indicate that the lateral dynamic responses of the GBF are more affected by the thrust force than by the distributed force when the wind loads are only considered; the maximum dynamic displacement of the GBF caused by the drag force is almost the same as that of the GBF caused by the inertia force when the wave loads are only considered, and the dynamic response of the GBF under combined wind and wave loads show a similar trend to that of the GBF under the wind loads only, especially the existence of a large displacement on the horizontal direction at the beginning of the loading.

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track geometry parameters. However, the relationship between railway track geometry and rail wear remains unclear. It is hard to acquire rail wear data for different alignments with varying geometric parameters during the alignment design phase. This study develops a PSO-ANN model to establish the mapping relationship between railway track geometry and rail wear, enabling prediction of rail wear based on track geometry parameters. The model achieves prediction accuracies of 96.70% for inner rail wear and 98.13% for outer rail wear. Compared with the conventional ANN model, the PSO-ANN model reduces the prediction errors by 22.54% for inner rail wear and 55.69% for outer rail wear. Sobol sensitivity analysis is conducted to analyze the influence of the track geometry parameters on rail wear, revealing that inner rail wear is mainly affected by curve radius, transition curve length, and superelevation, while outer rail wear is predominantly influenced by curve radius.

Curve rail grinding has always been one of the key points of subway maintenance and repair. The appropriate grinding can effectively reduce wear. A multi-objective optimization design method for grinding profiles in curved sections is proposed. Firstly, representative grinding profiles are selected as the initial population using the dynamic time regularization algorithm (DTW). Then, the optimal design range is determined based on wear characterization analysis, and mathematical expressions of wheel profiles are chosen as design variables to establish a parametric model. Next, the prediction model considering the evolution of wheel wear is incorporated into the multi-objective function, and objective function adaptive weight adjustment coefficient factors are introduced to establish the multi-objective optimization model for wheel profiles. The Latin hypercubic sampling method is employed to establish the RBF agent model for simulation calculation, and the optimization design of wheel profiles is carried out using the TS-NSGA-II multi-objective algorithm.

Aiming at the existing heavy‐haul railway, bridges hardly meet the transportation requirements. Based on the spatial vibration calculation model of the freight train–track–bridge (FTTB) system, the FTTB spatial vibration model under the condition of auxiliary steel beam reinforcement is established. Besides, according to the random analysis method of train derailment energy, coming up with an evaluation method of auxiliary steel beam reinforcement is based on safety and dynamic response, which is used to discuss the train safety and the change law of FTTB system vibration response. The results show that the derailment resistance of the FTTB system is increased by 22.6% after the auxiliary steel beam is reinforced. Compared with the previous speed (115.56 km/h), the speed is 132.73 km/h after the auxiliary steel beam reinforcement; at the same time, the allowable limit speed increases from 92.49 km/h to 106.18 km/h. In addition, the reinforcement of the auxiliary steel beam can not only effectively reduce the lateral vibration response of the FTTB system under the action of empty wagon but also effectively decline the vertical vibration response of the FTTB system under the action of the loaded wagon, which can meet the stability requirement for running at the speed of 90 km/h. In summary, the reinforcement of auxiliary steel beams can improve the running safety of trains, reduce the vibration response of the FTTB system, and meet the requirements of operation stability.