Designs

18 pages, 2632 KB

Open AccessArticle

A Digital Twin Design and Modeling Method for Steam Turbines Based on Reverse Modeling and Genetic Algorithm Optimization

by Pengfei Han, Botao Liu, Yuanqing Hu, Ming Li, Kedong Xie, Zhiyuan Cheng, Yingwei Liu, Wei Jiang, Minxun Zhang, Youtao Zhang and Junjie Hu

Designs 2026, 10(3), 55; https://doi.org/10.3390/designs10030055 (registering DOI) - 13 May 2026

Abstract

Monitoring the operating status of steam turbines is critical for efficient and safe power generation. This study proposes a digital twin design and modeling method for steam turbines, integrating reverse modeling techniques with genetic algorithm optimization. Firstly, the geometric design model is reconstructed [...] Read more.

Monitoring the operating status of steam turbines is critical for efficient and safe power generation. This study proposes a digital twin design and modeling method for steam turbines, integrating reverse modeling techniques with genetic algorithm optimization. Firstly, the geometric design model is reconstructed from 3D-scanned point cloud data using point cloud fusion and surface reconstruction technologies, covering 13 stages of high-pressure rotor blades, 9 stages of intermediate-pressure stator blades, and the cylinder. Secondly, a physics-based model suitable for computational fluid dynamics simulations is generated using optimized mesh design parameters. To address unmeasurable parameters, a genetic algorithm is applied for data-driven design optimization, enhancing the dynamic simulation accuracy to 95%. Finally, leveraging a reduced-order model, real-time mapping of key physical fields is achieved. The effectiveness of the design methodology is validated under typical deep peak-shaving operating conditions. Full article

(This article belongs to the Topic Digital Manufacturing Technology)

► Show Figures

Figure 1

18 pages, 1630 KB

Open AccessArticle

Ecodesign Strategies for Battery Enclosures: A Design-Driven Approach to Modularity, Single-Materiality and Circularity

by Antoni Lara, Albert Cruz, Sylvia Andrea Cruz, Magnus Carl Fredrik Eriksson, Antonio Confalonieri and Andreu Sanz

Designs 2026, 10(3), 54; https://doi.org/10.3390/designs10030054 (registering DOI) - 12 May 2026

Abstract

The environmental impact of battery systems is strongly influenced by early design decisions related to materials, structural architecture and assembly strategies. While extensive research addresses battery performance and recycling processes, fewer studies focus on how ecodesign principles can be systematically translated into concrete [...] Read more.

The environmental impact of battery systems is strongly influenced by early design decisions related to materials, structural architecture and assembly strategies. While extensive research addresses battery performance and recycling processes, fewer studies focus on how ecodesign principles can be systematically translated into concrete design solutions at the product level. This article presents an ecodesign strategy applied to the development of a battery enclosure from an industrial design perspective. The proposed approach combines the use of aluminium with high recycled content, a modular enclosure based on extruded profiles adaptable to different battery sizes, a single-material architecture enabled by welded joints, and reversible fastened connections to support assembly, disassembly and repairability. The article discusses how ecodesign criteria such as material efficiency, circularity, modularity and design for assembly and disassembly (DfA/DfD) can be embedded into a coherent battery enclosure concept, while also addressing the main limitations and trade-offs of the proposed strategy. Full article

(This article belongs to the Section Mechanical Engineering Design)

► Show Figures

Graphical abstract

39 pages, 27209 KB

Open AccessReview

The Role of Additive Manufacturing in the Design of Smart and Nature-Based Construction Systems: A Critical Review

by Antreas Kantaros, Alexandra Tsatsou, Zoe Kanetaki, Theodore Ganetsos, Constantinos Stergiou, Michail Papoutsidakis and Evangelos Pallis

Designs 2026, 10(3), 53; https://doi.org/10.3390/designs10030053 (registering DOI) - 9 May 2026

Abstract

This work examines the contribution of additive manufacturing as an enabling technology in the design and development of smart and sustainable construction systems, with particular emphasis on nature-based solutions. While the existing literature has devoted considerable attention to the material properties of additive [...] Read more.

This work examines the contribution of additive manufacturing as an enabling technology in the design and development of smart and sustainable construction systems, with particular emphasis on nature-based solutions. While the existing literature has devoted considerable attention to the material properties of additive manufacturing, much less emphasis has been placed on its role in design processes, prototyping, and decision-making in construction and urban systems. To address this gap, this study presents a comprehensive bibliometric analysis of the intersection between smart city frameworks and 3D printing technologies, utilizing a dataset of 103 peer-reviewed publications retrieved from the Scopus database. Using keyword co-occurrence analysis and network mapping through VOSviewer, this study identifies dominant thematic structures, core research hubs, and evolving trends within the field. Complementing this bibliometric analysis with qualitative synthesis, it also reveals a significant convergence of digital design, smart cities, and sustainability strategies. This work further highlights the contribution of additive manufacturing to design processes through rapid prototyping, customization, and the exploration of design alternatives. Rather than framing additive manufacturing as a replacement for conventional design practices, this study positions it as a complementary design capability that can enhance the design process, while also acknowledging important challenges related to scaling, regulation, and integration into construction workflows. This review concludes by outlining future research directions for strengthening the design-oriented integration of additive manufacturing within smart construction systems. Full article

(This article belongs to the Special Issue Design Process for Additive Manufacturing, 2nd Edition)

► Show Figures

Graphical abstract

5 pages, 162 KB

Open AccessEditorial

Sustainable Construction: Innovations in Design, Engineering, and the Circular Economy

by Aziz Ahmed

Designs 2026, 10(3), 52; https://doi.org/10.3390/designs10030052 - 8 May 2026

Abstract

The construction industry currently operates at a critical threshold [...] Full article

(This article belongs to the Special Issue Sustainable Construction: Innovations in Design, Engineering, and the Circular Economy)

56 pages, 3798 KB

Open AccessArticle

Simultaneous Integration of Photovoltaic Systems and Capacitor Banks in Radial Distribution Networks via the Grey Wolf Optimizer for Steady-State Voltage Regulation and Active Power Loss Minimization

by Susan Murillo and Alexander Aguila Téllez

Designs 2026, 10(3), 51; https://doi.org/10.3390/designs10030051 - 8 May 2026

Abstract

This paper presents a structured planning framework for the coordinated integration of photovoltaic (PV) systems and capacitor banks (CBs) in radial distribution networks to improve steady-state voltage regulation and reduce active-power losses. The proposed methodology combines deterministic power-flow assessment, index-based candidate screening, and [...] Read more.

This paper presents a structured planning framework for the coordinated integration of photovoltaic (PV) systems and capacitor banks (CBs) in radial distribution networks to improve steady-state voltage regulation and reduce active-power losses. The proposed methodology combines deterministic power-flow assessment, index-based candidate screening, and constrained joint placement and sizing using the Grey Wolf Optimizer (GWO) with an embedded CAPEX proxy. Compared with PV-only integration, the coordinated PV–CB strategy provides a more effective improvement in steady-state electrical performance, particularly in terms of slack-bus power factor and voltage regulation. In addition, relative to fixed coordinated PV–CB scenarios, the GWO-based formulation yields more balanced technical–economic solutions by improving power factor and voltage conditions while avoiding unnecessary overdimensioning of installed capacity. On the IEEE 15-bus system, the optimized configuration achieves a 45.9% reduction in active-power losses, improves the slack-bus power factor to 0.947, and reduces the average voltage deviation to 2.57%, with convergence reached in approximately 16 iterations. On the IEEE 34-bus system, the optimized solution yields a 49.8% loss reduction, increases the slack-bus power factor to 0.955, and reduces the average voltage deviation to 2.39%, with convergence reached in approximately 133 iterations. Using an energy price of 8.14 ctUSD/kWh, the corresponding annual loss–cost savings are approximately 19,975 USD and 78,475 USD for the IEEE 15- and 34-bus systems, respectively. The results demonstrate that the proposed GWO-based coordinated planning approach can achieve electrically effective and economically feasible solutions through the combined provision of local active-power injection and reactive-power compensation in radial distribution networks under steady-state operating conditions. Full article

(This article belongs to the Section Energy System Design)

► Show Figures

Figure 1

35 pages, 30281 KB

Open AccessArticle

Comparative Life Cycle Assessment of Topology Optimization and Generative Design for Sustainable Additively Manufactured Furniture

by Christina Kostopoulou, Vasileios D. Sagias, Paraskevi Zacharia, Antreas Kantaros and Constantinos Stergiou

Designs 2026, 10(3), 50; https://doi.org/10.3390/designs10030050 - 8 May 2026

Abstract

Sustainability is an increasingly important objective in design and engineering, yet the environmental implications of advanced computational design methods remain insufficiently quantified. This study examines the contribution of topology optimization to sustainable product development when applied exclusively to a product’s internal structure, while [...] Read more.

Sustainability is an increasingly important objective in design and engineering, yet the environmental implications of advanced computational design methods remain insufficiently quantified. This study examines the contribution of topology optimization to sustainable product development when applied exclusively to a product’s internal structure, while preserving external geometry, mechanical performance, and design intent. The furniture sector was selected as a representative case due to its significant environmental footprint and the strong role of aesthetic requirements within the design methodology. A gate-to-gate Life Cycle Assessment was performed to compare a conventionally designed stool with an internally optimized counterpart, both developed under the same design constraints and manufactured via Fused Deposition Modeling using Carbon Fiber-reinforced PETG (CF-PETG). The results indicate that computational strategies can reduce material waste by 57.8% to 90% compared to traditional subtractive methods. However, these benefits may be partially offset by increased energy demand during additive manufacturing due to geometric complexity. An additional comparative assessment involving generative design demonstrates that alternative computational strategies can achieve more balanced trade-offs between material efficiency and manufacturing energy, supporting sustainability while respecting design methodology constraints. Full article

► Show Figures

Graphical abstract

24 pages, 4786 KB

Open AccessArticle

Lightweight, Lateral and Sagittal Plane Symmetrical Biped Robot Design

by Davut Akdaş

Designs 2026, 10(3), 49; https://doi.org/10.3390/designs10030049 - 8 May 2026

Abstract

It is commonly noted in the literature that reducing mass and moment of inertia lowers the requirements for powerful electromechanical hardware and improves the overall energy efficiency of legged robots. For this reason, the humanoid robot RB2, the second of its kind at [...] Read more.

It is commonly noted in the literature that reducing mass and moment of inertia lowers the requirements for powerful electromechanical hardware and improves the overall energy efficiency of legged robots. For this reason, the humanoid robot RB2, the second of its kind at Balikesir University, has been developed iteratively. The motivation for this research is to design a lightweight, low-power humanoid robot to gain physical insight into the viability of using Delrin and 3D-printed ABS parts in its support structure and to enhance the robot’s efficiency in terms of weight and, as a result, power requirements. The number of degrees of freedom and the order of the joint motions of the planes are optimised to reduce moments of inertia and increase the range of motion of the robot’s legs. Additionally, the mechanical structure incorporates design features to facilitate assembly and maintenance. The newer robot’s weight is reduced to 25% of our first humanoid robot’s, while maintaining the same joint range of motion. Full article

► Show Figures

Graphical abstract

27 pages, 11688 KB

Open AccessArticle

Development of a Preliminary Renewable Energy Planning Tool with Storage and Carbon Footprint Assessment

by Xumiao Lin, Joana Correia, Miguel Marques, Ana Foles, José Silva, Teresa Batista, Carmen Luisa Vásquez Stanescu, Lucas Marinho and Fernando Barros

Designs 2026, 10(3), 48; https://doi.org/10.3390/designs10030048 - 7 May 2026

Abstract

The global transition toward a low-carbon economy has accelerated the adoption of renewable energy sources. This paper presents the development of a model-based electronic Decision Support System for renewable energy planning, incorporating energy storage and carbon footprint assessment. The tool assists stakeholders in [...] Read more.

The global transition toward a low-carbon economy has accelerated the adoption of renewable energy sources. This paper presents the development of a model-based electronic Decision Support System for renewable energy planning, incorporating energy storage and carbon footprint assessment. The tool assists stakeholders in the preliminary evaluation of local wind and solar resources. To validate the model’s credibility, a comparative analysis was conducted, using the Port of Sines, Portugal, as an industrial case study. Solar energy estimations were benchmarked against PVSyst, while wind energy simulations were compared with an INEGI technical study. Results indicate consistency in solar estimates, with maximum deviations of 14% for fixed installations and 13% for vertical barriers, primarily due to terrain orography that was not yet integrated into the algorithm. Regarding wind energy, deviations reached 19% to 25%, largely resulting from the use of aggregated mean values in the reference data and generic turbine models. Overall, this work contributes to energy engineering by formalizing a validated workflow that facilitates early-stage sizing and strategic investment decisions under conditions of data scarcity. The tool proves effective for rapid screening of promising investment options while maintaining a balance between computational complexity and practical usability. Full article

(This article belongs to the Section Energy System Design)

► Show Figures

Figure 1

23 pages, 14314 KB

Open AccessReview

System-Level Design of Photovoltaic Recycling Infrastructure: A Review of Mechanical, Thermal, Chemical, and Laser-Based Technologies

by Mahmoud Dhimish and Peter Behrensdorff Poulsen

Designs 2026, 10(3), 47; https://doi.org/10.3390/designs10030047 - 29 Apr 2026

Abstract

This review paper presents a system-level engineering design perspective on end-of-life (EoL) photovoltaic (PV) recycling, addressing a critical gap in the literature that is predominantly focused on material and process-level analyses. A unified framework is developed to evaluate mechanical, thermal, chemical, and emerging [...] Read more.

This review paper presents a system-level engineering design perspective on end-of-life (EoL) photovoltaic (PV) recycling, addressing a critical gap in the literature that is predominantly focused on material and process-level analyses. A unified framework is developed to evaluate mechanical, thermal, chemical, and emerging laser-based technologies through the lenses of system architecture, process control, and infrastructure integration. The study introduces design-oriented concepts, including optimal processing windows, modular system configurations, and multi-layer control frameworks, to support decision-making in scalable PV recycling systems. Particular emphasis is placed on laser-based recycling (e.g., femtosecond laser technology), which enables non-thermal, high-precision, and interface-selective material separation, representing a paradigm shift towards intelligent and adaptive recycling infrastructures. The paper also highlights the transition from conventional bulk PV processing to precision-controlled, artificial intelligence (AI)-enabled systems, and outlines future research and industrial pathways required to realize sustainable, high-efficiency PV recycling within a circular economy. Full article

(This article belongs to the Section Smart Manufacturing System Design)

► Show Figures

Figure 1

35 pages, 5585 KB

Open AccessArticle

A General Procedure for Basic Kinematic Chain Formation and Topology Selection for Planar Mechanisms

by Arthur Erdman, John Titus, Mahmud Suhaimi Ibrahim and Sean Mather

Designs 2026, 10(3), 46; https://doi.org/10.3390/designs10030046 - 27 Apr 2026

Abstract

In a complete kinematic synthesis process, a designer must select a planar linkage topology that is well suited to their problem situation. This involves weighing a set of competing priorities. For example, is it better to choose a simple topology like a four-bar [...] Read more.

In a complete kinematic synthesis process, a designer must select a planar linkage topology that is well suited to their problem situation. This involves weighing a set of competing priorities. For example, is it better to choose a simple topology like a four-bar mechanism that will be cheaper to produce, or a complex topology like an eight-bar mechanism that can produce intricate motions but will also be more expensive and more difficult to synthesize? The process of selecting the topology is broadly known as type synthesis, or sometimes structure synthesis, and has been studied in the past. However, past works on planar linkage type synthesis have overemphasized isomorphism detection, identifying the complete set of unique topologies up to a certain number of links, while the central problem of choosing the ideal topology has often been overlooked. In this work, a general procedure for forming basic kinematic chains (BKCs), a simplified topological representation, is presented. Then, a set of rules and design principles is provided that can help a designer narrow the infinite possible BKC options down to a manageable set. A few practical examples are provided to demonstrate the concepts and show that the procedure is effective. A literature review is also provided that examines past works, as well as introducing alternative approaches, such as simultaneous algorithmic methods and spatial methods. Full article

(This article belongs to the Section Mechanical Engineering Design)

► Show Figures

Figure 1

16 pages, 3647 KB

Open AccessArticle

Mitigating Stress Shielding in Dorr C Femurs via Additive Manufacturing: A Proof-of-Concept Numerical Analysis

by Roberta Cromi, Francesca Berti, Matteo Gavazzoni, Luigi La Barbera, Dalila Di Palma, Sara Maggioni, Jacopo Menini, Massimo Franceschini, Stefano Foletti and Tomaso Villa

Designs 2026, 10(3), 45; https://doi.org/10.3390/designs10030045 - 23 Apr 2026

Abstract

Bone resorption secondary to stress shielding is a leading cause of hip implant failure, primarily due to the stiffness mismatch between the femur and the prosthesis. Although anatomical stem designs generally provide improved load transfer, Dorr type C femurs often require straight stems [...] Read more.

Bone resorption secondary to stress shielding is a leading cause of hip implant failure, primarily due to the stiffness mismatch between the femur and the prosthesis. Although anatomical stem designs generally provide improved load transfer, Dorr type C femurs often require straight stems to ensure adequate primary stability. This work presents a systematic approach to designing a straight, additively manufactured porous titanium hip stem aimed at minimizing stress shielding. The lattice architecture is customized to replicate the mechanical properties of bone based on patient-specific femoral CT scans. The performance of the resulting porous implant is numerically assessed under simplified physiological gait loading conditions. The implant behavior is evaluated through a homogenization strategy to model the lattice structure, significantly reducing the computational effort and making the methodology easily replicable. Compared to its full counterpart, the porous design achieves a significant reduction in predicted bone loss, suggesting that the proposed framework is a promising proof of concept for patient-specific implants. While further experimental validation and larger cohort studies are required, these findings highlight the potential of mechanically tunable porous structures to mitigate the stress shielding phenomenon in anatomical conditions such as Dorr type C femurs, which require straight stems. Full article

(This article belongs to the Special Issue Advanced Design Methodologies for Additive Manufacturing in Patient-Specific Medical Solutions)

► Show Figures

Figure 1

24 pages, 6320 KB

Open AccessArticle

Crashworthiness Optimization of Composite/Metal Hybrid Tubes with Triggering Holes

by Yan Ma, Zehui Huang, Hongbin Tang, Jianjiao Deng, Jingchun Wang, Shibin Wang, Zhiguo Zhang and Zhenjiang Wu

Designs 2026, 10(2), 44; https://doi.org/10.3390/designs10020044 - 10 Apr 2026

Abstract

Due to high specific energy absorption, composite/metal hybrid multi-cell thin-walled tubes hold significant potential in the field of automotive passive safety. However, the material coupling effect enhancing SEA often elevated the initial peak crushing force, reducing crushing force efficiency and compromising occupant protection. [...] Read more.

Due to high specific energy absorption, composite/metal hybrid multi-cell thin-walled tubes hold significant potential in the field of automotive passive safety. However, the material coupling effect enhancing SEA often elevated the initial peak crushing force, reducing crushing force efficiency and compromising occupant protection. To balance SEA and CFE, trigger holes were introduced as an induced deformation mechanism for hybrid tubes to reduce IPCF while preserving SEA, with the optimized perforated configuration yielding higher CFE than the non-perforated counterpart. A high-fidelity finite element model of the hybrid tube was developed and experimentally validated, and the influences of induced structural parameters on SEA and CFE were investigated. Given the strong nonlinear coupling between trigger parameters and crashworthiness, a multilayer perceptron surrogate model was constructed using 200 optimal Latin hypercube sampling samples (20 for validation). A Q-learning enhanced particle swarm optimization (QL-PSO) algorithm was adopted for optimization, with reinforcement learning dynamically adjusting PSO parameters to balance global exploration and local exploitation. Finite element simulations validated that the proposed method achieved a favorable SEA-CFE trade-off, with SEA and CFE improved by 12.02% and 16.39% respectively, outperforming reported configurations. Compared with standard PSO, QL-PSO exhibited superior search efficiency and inverse mapping accuracy, with 22% higher optimization efficiency and full compliance with inverse design performance targets. This study provided valuable guidance for the design of thin-walled energy-absorbing structures in multi-material vehicle bodies. Full article

(This article belongs to the Section Vehicle Engineering Design)

► Show Figures

Figure 1

35 pages, 12654 KB

Open AccessArticle

An Integrated BIM–NLP Framework for Design-Informed Automated Construction Schedule Generation

by Mahmoud Donia, Emad Elbeltagi, Ahmed Elhakeem and Hossam Wefki

Designs 2026, 10(2), 43; https://doi.org/10.3390/designs10020043 - 7 Apr 2026

Abstract

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily [...] Read more.

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. Full article

(This article belongs to the Special Issue Quantum and AI Technologies in Engineering and Construction Projects: Design Challenges and Opportunities)

► Show Figures

Figure 1

24 pages, 7253 KB

Open AccessArticle

On the Design of Smooth Curvature Tunable Paths for Safe Motion of Autonomous Vehicles

by Gianfranco Parlangeli

Designs 2026, 10(2), 42; https://doi.org/10.3390/designs10020042 - 7 Apr 2026

Abstract

Navigation is an essential ability for autonomous systems, and efficient motion planning for mobile robots is a central topic for autonomous vehicle design and service robotics. Most path-planning algorithms produce reference paths with sharp or discontinuous turns, inducing several drawbacks during mission execution, [...] Read more.

Navigation is an essential ability for autonomous systems, and efficient motion planning for mobile robots is a central topic for autonomous vehicle design and service robotics. Most path-planning algorithms produce reference paths with sharp or discontinuous turns, inducing several drawbacks during mission execution, such as unexpected inertial stress and strain on the mechanical structure, passenger discomfort, and unsafe and unpredictable deviation of the real trajectory with respect to the reference planned one. Oppositely, smooth and feasible trajectories are often desired in real-time navigation for nonholonomic mobile robots where the surrounding environment can have a dynamic and complex shape with obstacles. In this paper, we propose a novel technique for the generation of smooth, collision-free, and near time-optimal paths for nonholonomic mobile robots. The proposed method exploits the features of a set of tunable bump functions, with the goal of pursuing smooth reference curves with tunable features (such as curvature, or jerk) yet seeking a reasonable length minimality, thus combining the advantages of the two most adopted techniques, namely Bezier interpolation and Dubins curves. After a thorough description of the analytical methods, the paper is primarily concerned with the design and tuning methods of the path-planning algorithm. Both a graphical method and numerical investigations and examples are performed to fully exploit the algorithm potentialities and to show the efficiency of the proposed strategy. Full article

(This article belongs to the Collection Editorial Board Members’ Collection Series: Designs of New Generation Vehicles)

► Show Figures

Figure 1

23 pages, 1896 KB

Open AccessArticle

Research on Green Flexible Job Shop Rescheduling with Urgent Order Insertion and Multi-Speed Machines: A Model and an Improved MOEA/D Algorithm

by Tao Yang and Hanning Chen

Designs 2026, 10(2), 41; https://doi.org/10.3390/designs10020041 - 3 Apr 2026

Abstract

This paper investigates a tri-objective green flexible job shop rescheduling problem under urgent order insertion and multi-speed machining conditions, where makespan, total energy consumption, and total tool wear are jointly optimized. First, an event-driven freezing mechanism is introduced, in which completed and ongoing [...] Read more.

This paper investigates a tri-objective green flexible job shop rescheduling problem under urgent order insertion and multi-speed machining conditions, where makespan, total energy consumption, and total tool wear are jointly optimized. First, an event-driven freezing mechanism is introduced, in which completed and ongoing operations are fixed, while only the rescheduling window composed of waiting operations and urgent-order operations is re-optimized. On this basis, two rescheduling strategies, namely complete rescheduling and deferred rescheduling, are designed and compared. Second, to improve the solution capability in complex dynamic environments, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) with a three-layer encoding scheme is proposed. The algorithm incorporates hybrid initialization, tabu-guided crossover, simulated annealing mutation, and critical-path-based variable neighborhood search. Experimental results show that the proposed method performs well in energy consumption optimization and tool wear control, while effectively improving the diversity and distribution quality of the Pareto solution set. Further analysis indicates that deferred rescheduling generally outperforms complete rescheduling, while the original-orders-first and urgents-first strategies exhibit different strengths in convergence, solution quality, and objective optimization. The proposed study provides an effective modeling and optimization framework for multi-objective green rescheduling problems and offers theoretical support for production scheduling decisions that need to balance production efficiency, energy saving, and tool-related cost control in practical manufacturing systems. Full article

(This article belongs to the Topic Distributed Optimization for Control, 2nd Edition)

► Show Figures

Figure 1

29 pages, 1851 KB

Open AccessSystematic Review

Technological Trends in Lean Construction for Engineering Design Improvement and Productivity in Civil Engineering Projects: A Systematic Literature Review

by Luis Mayo-Alvarez, Jorge Córdova-Maraví, Diego García-Gómez and Iván Paredes-Julca

Designs 2026, 10(2), 40; https://doi.org/10.3390/designs10020040 - 1 Apr 2026

Abstract

Lean Construction has become a key strategy for improving productivity, reducing waste, and increasing efficiency in civil engineering projects. In parallel, advances in digital technologies have transformed the way engineering design and project planning processes are conceived and managed. However, there remains a [...] Read more.

Lean Construction has become a key strategy for improving productivity, reducing waste, and increasing efficiency in civil engineering projects. In parallel, advances in digital technologies have transformed the way engineering design and project planning processes are conceived and managed. However, there remains a limited systematic understanding of how emerging technologies support engineering design practices and influence the implementation and performance of Lean Construction in diverse civil engineering scenarios. This study presents a systematic literature review of 70 peer-reviewed articles published between 2019 and 2025, following the PRISMA 2020 guidelines. The selected studies were examined using a structured classification framework consisting of three analytical categories: Technologies and Tools, Construction Methods and Sustainability, and Production Philosophies and Management. From an engineering design perspective, this framework allows the identification of technological trends, design-support tools, and management strategies that influence the planning, modeling, and optimization of construction processes. The results show that digital technologies, such as Building Information Modeling (BIM), automation systems, Artificial Intelligence, and Industry 4.0 tools, play a significant role in supporting engineering design activities by improving project visualization, coordination, and decision-making during the design and planning stages. These technologies contribute to more integrated design processes aligned with Lean Construction principles. At the same time, the analysis reveals that the adoption of Lean Construction technologies varies depending on project characteristics, levels of digital maturity, and regional industry conditions. The main barriers identified in the literature include interoperability limitations, insufficient workforce training, and organizational resistance to technological change. Overall, the review provides a structured synthesis of recent research trends and highlights the technological and managerial factors that influence the successful integration of Lean Construction with engineering design practices in civil engineering. The findings contribute to bridging the gap between technological innovation, design methodologies, and Lean Construction implementation, offering insights for both researchers and practitioners seeking to improve efficiency, sustainability, and design performance in construction projects. Full article

(This article belongs to the Special Issue Quantum and AI Technologies in Engineering and Construction Projects: Design Challenges and Opportunities)

► Show Figures

Figure 1

23 pages, 4551 KB

Open AccessArticle

Simulation-Driven Screening and Machine Learning Surrogate Modelling of Water Pipeline Start-Up and Filling Operations for Engineering Design Support

by Aiken H. Ortega-Heredia, Oscar E. Coronado-Hernández and Vicente S. Fuertes-Miquel

Designs 2026, 10(2), 39; https://doi.org/10.3390/designs10020039 - 1 Apr 2026

Cited by 1

Abstract

Filling operations in pressurised pipeline systems can trap air pockets, generating hazardous transient overpressures that threaten structural integrity and operational reliability. Evaluating these events using conventional hydraulic models can be computationally intensive, limiting design-space exploration of operational scenarios. This study presents a simulation-driven [...] Read more.

Filling operations in pressurised pipeline systems can trap air pockets, generating hazardous transient overpressures that threaten structural integrity and operational reliability. Evaluating these events using conventional hydraulic models can be computationally intensive, limiting design-space exploration of operational scenarios. This study presents a simulation-driven design-screening framework based on Monte Carlo simulation to evaluate and predict peak absolute pressures during pipeline start-up and filling operations. A total of 2000 transient scenarios were generated for a representative 1100 m pipeline system by varying key geometric and operational parameters, including diameter, friction factor, column lengths, slopes, and reservoir elevation. Twenty-eight machine learning regression models were trained to develop a physics-informed surrogate model capable of rapidly predicting pressure peaks within the defined parameter domain. The trilayered neural network achieved the highest predictive accuracy, with robust validation (RMSE = 10.95 m, R² = 0.99) and test performance (RMSE = 9.78 m, R² = 0.99). Screening results showed that nominal pressure thresholds of 61.18 m and 407.89 m were exceeded in 97.53% and 4.89% of the retained peak-forming scenarios (n = 1746), respectively. The proposed framework provides an efficient and reproducible surrogate-based design-screening approach for transient overpressure risk within the evaluated hydraulic domain. Full article

► Show Figures

Figure 1

26 pages, 1036 KB

Open AccessArticle

Translating Design Language into Fabricated Form: A Style-Oriented Framework for Desktop Additive Manufacturing of Twentieth-Century Interiors

by Antreas Kantaros, George Sakellaropoulos, Theodore Ganetsos and Nikolaos Laskaris

Designs 2026, 10(2), 38; https://doi.org/10.3390/designs10020038 - 1 Apr 2026

Abstract

Digital fabrication technologies increasingly enable designers and researchers to reinterpret historical design languages through contemporary production methods. Within this context, desktop 3D printing offers an accessible yet constrained medium for translating stylistically rich interior design objects into tangible form. This study examines how [...] Read more.

Digital fabrication technologies increasingly enable designers and researchers to reinterpret historical design languages through contemporary production methods. Within this context, desktop 3D printing offers an accessible yet constrained medium for translating stylistically rich interior design objects into tangible form. This study examines how distinct twentieth-century interior design movements—Art Deco, Bauhaus, and Mid-century Modern—are mediated through desktop additive manufacturing, focusing on the preservation of formal identity rather than manufacturing performance. Representative interior objects were digitally reconstructed from archival and reference material and fabricated under standardized desktop 3D printing conditions. The investigation adopts a style-oriented evaluation framework that examines silhouette continuity, characteristic geometric features, ornamental legibility, and structural–stylistic coherence. To support comparative interpretation, a Style Preservation Index (SPI) is introduced as a structured design evaluation tool that makes stylistic assessment explicit and repeatable without reducing it to purely geometric metrics. The results demonstrate that stylistic legibility is preserved to differing degrees depending on the formal vocabulary of each design movement, with minimal and geometrically rational styles exhibiting higher compatibility with layer-based fabrication than ornamentally dense or materially expressive designs. Rather than framing these differences as technological limitations, the study interprets them as insights into how design languages interact with fabrication constraints. By positioning desktop additive manufacturing as a medium of design translation rather than replication, this work contributes a reproducible framework for design research, heritage interpretation, and education, offering a structured approach for exploring how historical styles can be re-engaged through contemporary digital fabrication. Full article

► Show Figures

Figure 1

44 pages, 4569 KB

Open AccessArticle

LSTM-Based Fast Prediction of Seismic Response and Fragility for Bridge Pile-Group Foundations: A Data-Driven Design Approach

by Zhenfeng Han, Deming She and Jun Liu

Designs 2026, 10(2), 37; https://doi.org/10.3390/designs10020037 - 23 Mar 2026

Cited by 1

Abstract

Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated [...] Read more.

Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated deep learning framework is proposed that employs a unidirectional, multi-layer LSTM network for end-to-end prediction of structural responses directly from ground motions. The proposed model features two innovations. First, its multi-output capability enables simultaneous prediction of complete response time histories and peak values for key engineering demand parameters—bending moment, curvature, and pile cap displacement. Second, the network incorporates sliding time windows and residual connections to capture complex nonlinear soil–structure interaction. These predictions are integrated into a probabilistic seismic demand model to generate fragility curves. The framework is validated using a high-fidelity OpenSees model of a real bridge PGF subjected to 1000 ground motions. Results demonstrate the model’s excellent predictive accuracy: for peak bending moment, the mean predicted-to-actual ratio ranges from 0.97 to 1.03, with standard deviation below 0.12; the derived fragility curves show excellent agreement with benchmarks, achieving an average R² of 0.985 across four damage states. More importantly, the framework reduces the time for a complete fragility assessment (200 incremental dynamic analyses) from approximately 12 h to about 1 s—a 40,000× speed-up—making data-driven rapid and large-scale seismic risk assessment a reality. The proposed framework provides engineers with a practical design tool for rapidly evaluating alternative foundation configurations and informing seismic design decisions, thereby integrating advanced data-driven methods directly into the engineering design workflow. Full article

(This article belongs to the Special Issue Intelligent Infrastructure and Construction in Civil Engineering)

► Show Figures

Figure 1

Journal Description

Designs

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI