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The full exploitation of offshore wind resources can essentially satisfy the massive energy demand. The realization and application of ultra-high-power offshore wind turbines are crucial to achieving full use of deep-sea wind energy and reducing the cost of wind power. For the VolturnUS-S semi-submersible floating foundation of a 15 megawatt (MW) offshore wind turbine, the effect of structural parameters on hydrodynamic performance was investigated by controlling the variables described in this paper. Accordingly, the floating foundation was optimized and coupled to the 15 MW offshore wind turbine. The dynamic performance of the integrated 15 MW offshore wind turbine was analyzed under different operating conditions, by applying the aero-hydro-servo-elastic coupled method. The results show that for a wave in a 0-degree direction, a 5% increase of column spacing will reduce the peak value of the pitch transfer function by 33.61%, and that a 5% decrease of the outer column diameter will further reduce the peak value by 26.27%. The standard deviation of the time-domain surge responses was reduced by 19.78% for the optimized offshore wind turbine, and the maximum value of the mooring line tension was reduced by 13.55% under normal operating conditions.

The problem of synchronization of product manufacturing productivity and determining the structure and parameters of production sites of technological complexes of mechanical assembly production is considered. Dependences are proposed that make it possible to determine the organizational scheme of the manufacturing of products on the production sites of a technological complex for a given output rate of finished products. A method for determining the structure and parameters of production sites of the technological complex of mechanical assembly production is formalized.

A method and an algorithm are proposed for solution of multicriterion problems of structural and parametric optimization of nonhomogeneous anisotropic constructive elements under the condition of the choice of an acceptable hierarchical structure of a complex construction, an appropriate distribution of requirements on each functional constructive element of each hierarchical level, and a reasonable trade-off between inconsistent requirements on the strength, reliability, and adaptability to manufacture and technical and economic efficiency of the construction, taking into account the risk degree and risk level of non-nominal situations. The efficiency of the method proposed is illustrated by an example of choice of an acceptable structure of a complex system, namely, a wind turbine. [PUBLICATION ABSTRACT]

The use of current computer tools in both manufacturing and design stages breaks with the traditional conception of productive process, including successive stages of projection, representation, and manufacturing. Designs can be programmed as problems to be solved by using computational tools based on complex algorithms to optimize and produce more effective solutions. Additive manufacturing technologies enhance these possibilities by providing great geometric freedom to the materialization phase. This work presents a design methodology for the optimization of parts produced by additive manufacturing and explores the synergies between additive manufacturing, parametric design, and optimization processes to guide their integration into the proposed methodology. By using Grasshopper, a visual programming application, a continuous data flow for parts optimization is defined. Parametric design tools support the structural optimization of the general geometry, the infill, and the shell structure to obtain lightweight designs. Thus, the final shapes are obtained as a result of the optimization process which starts from basic geometries, not from an initial design. The infill does not correspond to pre-established patterns, and its elements are sized in a non-uniform manner throughout the piece to respond to different local loads. Mass customization and Fused Deposition Modeling (FDM) systems represent contexts of special potential for this methodology.

Traditional structural design involves drawing recognition, repeated modeling, parameter tuning, and numerous mechanical analyses by skilled designers, which is time-consuming and inefficient. To address those problems, a two-stage automatic structural design of the steel frame based on expert experiences is proposed. In the first stage, from the computer-aided design plain drawing, semantic features (walls and openings) and geometrical information of architectural elements are extracted by a layer classification method. The segmentation of rooms is conducted by an enclosed region detection method and the connectivity graph is generated using the connected component analysis method. Based on expert experiences considering both structure and architectural function requirements, the structural member configuration and the floor load distribution are automatically established to obtain the parametric structural model. In the second stage, a modified particle swarm optimization (MPSO) based on expert experiences is proposed for single-objective structural optimization according to the design codes. Then based on MPSO, a hierarchical multi-objective optimization method is adopted to obtain more available solutions with different economic benefit and redundant safety. The results show that the proposed two-stage structural design framework is fully automatic and highly efficient. It integrates parametric modeling and structural optimization, and also enables effective transfer of different data items including architectural plan and structural model. It provides a guideline to automatic structural design of steel frames.

Estimating structural models is often viewed as computationally difficult, an impression partly due to a focus on the nested fixed-point (NFXP) approach. We propose a new constrained optimization approach for structural estimation. We show that our approach and the NFXP algorithm solve the same estimation problem, and yield the same estimates. Computationally, our approach can have speed advantages because we do not repeatedly solve the structural equation at each guess of structural parameters. Monte Carlo experiments on the canonical Zurcher bus-repair model demonstrate that the constrained optimization approach can be significantly faster.

In the past, many studies have been conducted on the optimization of reinforced concrete (RC) structures. These studies have demonstrated the effectiveness of different optimization techniques to obtain an economical design. However, the use of optimization techniques to an obtain economical design is not so practical due to the difficulty in applying most of the optimization techniques to achieve an optimal solution. The RC beam is one of the most common structural elements encountered by a practising design engineer. The current study is designed to highlight the potential of the Solver tool in MS Excel as an easy-to-use option for optimizing the design of simply supported RC beams. A user-friendly interface was developed in a spreadsheet in which beam design parameters from a typical design can be entered and an economical design can be obtained using the Evolutionary Algorithm available in the MS Excel Solver tool. To demonstrate the effectiveness of the developed optimization tool, three examples obtained from the literature have been optimized. The results showed that up to 24% economical solution can be obtained by keeping the same material strengths that were assumed in the original design. However, if material strength is also considered as a variable, up to 44% of the economical solution can be obtained. A parametric study was also conducted to investigate the effect of different design variables on the economical design of simply supported RC beams and to derive useful rules of thumb for their design and proportioning, with the objective of cost minimization. The results of the parametric study suggest that the grade of the reinforcing steel is one of the most influential factors that affect the cost of simply supported RC beams. Practicing engineers can use the trends derived from this research to further refine their optimal designs.

Reinforced concrete arch bridges are susceptible to non-stationary degradation under combined environmental and load effects, rendering traditional reliability assessments based on stationary assumptions inadequate. To address this gap, this study first derived a reliability calculation method tailored for non-stationary degradation scenarios. Subsequently, an ISSA-Kriging surrogate model was proposed for the reliability evaluation of reinforced concrete arch bridges, with validation and analysis conducted using the Shuiluo River Bridge as an engineering case. Results indicate that the ISSA-Kriging model achieves high prediction accuracy: its sample response error is controlled within 4% in repeated random sampling tests, and its accuracy is approximately 60% higher than that of the standard Kriging model. The model reliably fits the time-varying reliability curve of the main arch ring, confirming its suitability for large-scale parametric analysis and engineering optimization. Compared with stationary degradation, non-stationary degradation accelerates the decay rate of the main arch ring’s reliability index by 20%–30%. After 50 years of service, the reliability reduction rates of the arch springing, arch crown, and mid-span (1/2 arch ring) under non-stationary degradation reach 90.8%, 97.8%, and 52.7%, respectively, leading to an obvious “unimodal” reliability distribution across the semi-structure of the main arch ring. Additionally, non-stationary load fluctuations exacerbate structural damage accumulation, emphasizing the need for targeted durability protection of key components. A limitation of this study is that the proposed non-stationary degradation model, while theoretically consistent with non-stationary deterioration laws and validated via numerical simulation, lacks direct calibration with long-term on-site monitoring data. Future research will focus on integrating structural health monitoring data to dynamically revise the model, narrowing the gap between numerical simulation and actual structural performance, and thereby enhancing the engineering practical value of non-stationary reliability assessment results. This study provides a robust technical tool for the non-stationary reliability assessment of reinforced concrete arch bridges and offers guidance for durability design and maintenance optimization.

In this work, we study the potential of using kriging metamodelling to perform multi-objective structural design optimization using finite element analysis software and design standards while keeping the computational efforts low. A method is proposed, which includes sustainability and buildability objectives, and it is applied to a case study of reinforced concrete foundations for wind turbines based on data from a large Swedish wind farm project. Sensitivity analyses are conducted to investigate the influence of the penalty factor applied to unfeasible solutions and the size of the initial sample generated by Latin hypercube sampling. A multi-objective optimization is then performed to obtain the optimum designs for different weight combinations for the four objectives considered. Results show that the kriging-obtained designs from samples of 20 designs outperform the best designs in the samples of 1000 designs. The optimum designs obtained by the proposed method have a sustainability impact 8–15% lower than the designs developed by traditional methods.

The use of parametric and multi-objective optimization (MOO) as a new way of approaching architectural design has been growing in line with current breakthroughs in computational architecture. Wood, on the other hand, is a living and unique building material that provides durability, manufacturing flexibility, and local availability. One of the structure types that provides high structural stability is the hyperboloid. However, the exploration of hyperboloid structures in building design, together with the building daylight objective, is still limitedly reported. This paper presents the application of the parametric approach and multi-objective optimization in optimizing the structure and daylight objectives of a hyperboloid two-story wooden house in Japan, made of 105 mm × 105 mm × 4000 mm Japanese timber. The method involves iterating dynamic parameters such as radius bottom, offset distance, timber members, twisting level, building height, radius-top, and roof slope to optimize the structural objective of minimizing normal force average, displacement, and cost while simultaneously maximizing building volume. Regarding daylight objectives, unit movement and glazing ratio that control the glazing strategies were explored to optimize useful daylight illumination (UDI) in summer and winter. The optimization and exploration yielded 10,098 solutions in structural analysis and 406 solutions in daylight exploration. Based on the data analysis, the proposed methodology has successfully produced the best design solution, discovering the balance between the objective trade-offs. In addition, the most influential parameter that shapes the value of design objectives has been identified. The findings of this research were expected to contribute to and enhance the performance-based design optimization, and support design decision-making process in the early design stage of a wooden house with a hyperboloid structure.