Li, M N; Wang, X; Cheng, R X and Chen, Y (2025) Aided design decision-making framework for engineering projects considering cost and social benefits. Engineering, Construction and Architectural Management, 32(8), pp. 5040-5065. ISSN 0969-9988
Abstract
Purpose – Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience. Design/methodology/approach – A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives. Findings – The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers. Originality/value – The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | aided design; bp neural network; cost prediction; engineering projects; multi-objective decision-making |
| Index terms: | cost prediction, case study, project design, back propagation, designer, screening, database, bp neural network, design framework, social benefit, drawing, neural network, decision-making, methodology, design stage, design decision |
| Subjects: | professional practice, contractual arrangements, algorithms, research methods, theoretical framing, data collection methods, profession, technical documentation, financial and cost management, artificial intelligence, management, design practice, decision analysis, data management |
| Topics: | Digital Applications, Design Practice, Human Resources, Roles and Professions, Cost Management, Research Practice, Risk Management, Procurement |
| Descriptive scope: | 5 PCTEA |
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