Journal of Architectural Engineering Technology
Open Access

Low-carbon materials; Sustainable construction; Embodied carbon; Green building; Geopolymer concrete; Cross-laminated timber; Circular economy; Carbon sequestration; Building Information Modeling (BIM); Eco-friendly materials; Climate-resilient infrastructure.

Introduction

Parametric design optimization (PDO) is a powerful computational methodology used to improve the performance, efficiency, and feasibility of products and systems by adjusting key design parameters [1]. This article provides a comprehensive overview of the fundamental principles, techniques, and applications of PDO. It explores optimization methods, including gradient-based and metaheuristic algorithms, and highlights real-world applications in architecture, engineering, manufacturing, and product design [2]. The article concludes by discussing challenges, future trends, and the role of artificial intelligence in advancing PDO. Parametric design is a process that uses mathematical algorithms and computational tools to define and control design parameters. By systematically varying these parameters, engineers and designers can optimize product performance, minimize costs, and enhance overall efficiency [3].

The construction industry is at a critical crossroads. As one of the most resource-intensive sectors globally, it accounts for approximately 39% of total energy-related carbon dioxide (CO₂) emissions, with 11% attributed to embodied carbon in building materials and construction processes [4]. Amid growing environmental concerns and the urgent need to meet the targets set by international climate agreements such as the Paris Accord, the sector must undergo a fundamental transformation. Central to this transformation is the adoption of low-carbon construction materials, which offer promising alternatives to traditional, carbon-intensive building products [5]. Low-carbon materials are designed or sourced to significantly reduce greenhouse gas emissions throughout their lifecycle from extraction and manufacturing to installation, use, and end-of-life disposal [6]. These materials are not only environmentally friendly but also offer additional benefits such as thermal efficiency, durability, and potential cost savings over time. Recent advances in material science, coupled with increasing environmental regulations and stakeholder awareness, are catalyzing a shift toward more sustainable construction paradigms [7].

This paper delves into the evolution, characteristics, and applications of low-carbon construction materials within the broader framework of sustainable development. By analyzing a variety of materials from bio-based products like hempcrete and bamboo, to industrial innovations like geopolymer cements and recycled aggregates, we seek to provide a holistic understanding of their role in achieving carbon neutrality in the built environment [8]. Furthermore, the paper investigates the synergies between low-carbon materials and emerging digital tools, such as Building Information Modeling (BIM), which can enhance precision in material use and life-cycle carbon tracking.

Ultimately, the transition to low-carbon construction is not merely a technical challenge, but also a socio-economic and policy issue. This study addresses the barriers and opportunities for integrating these materials into mainstream practice, offering strategic insights for policymakers, architects, engineers, and developers committed to climate-resilient and sustainable building practices.

Fundamental concepts of parametric design optimization

Parametric design relies on a set of parameters and their relationships to define the geometry and behavior of a system. The parameters can include:

• Geometric variables, length, width, height, angles.
• Material properties, density, elasticity, thermal conductivity.
• Performance metrics, strength, efficiency, cost, energy consumption.

By manipulating these parameters, designers can create numerous design iterations and evaluate their performance.

Optimization in parametric design involves identifying the best combination of parameters to achieve a predefined objective. This process typically includes:

Objective function, the goal to maximize or minimize (e.g., minimize weight, maximize efficiency).

Constraints, design limitations or boundaries (e.g., material limits, size restrictions).

Optimization algorithm, the method used to search for the optimal solution.

Optimization techniques in parametric design

Gradient-based methods use derivatives to find the direction of the steepest descent or ascent toward the optimal solution. These methods are effective for smooth and continuous functions but may struggle with complex, multi-modal problems.

• Steepest descent method
• Newton-Raphson method
• Conjugate gradient method

Metaheuristic algorithms are widely used in parametric design optimization due to their ability to handle complex, non-linear, and multi-objective problems.

Genetic algorithm (GA), inspired by natural selection, GA uses crossover and mutation operators to evolve optimal solutions.

Particle swarm optimization (PSO) based on the movement of particles in a swarm, this method searches for optimal solutions by adjusting particle positions.

Simulated annealing (SA) mimics the cooling process of metals to escape local optima and find global solutions.

Ant colony optimization (ACO), models the behavior of ants seeking the shortest path, useful for routing and path-planning problems.

In many design scenarios, multiple conflicting objectives need to be optimized simultaneously. Multi-objective optimization algorithms, such as NSGA-II (Non-dominated Sorting Genetic Algorithm) and MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition), are used to generate a Pareto front of optimal solutions.

Parametric optimization is widely used in architecture to design energy-efficient and aesthetically pleasing structures.

Optimizing the shape of a building façade to reduce energy consumption by controlling solar gain and natural ventilation.

Tools, Rhino + Grasshopper, Autodesk Dynamo.

Mechanical and aerospace engineering

PDO plays a crucial role in optimizing the performance of mechanical components and aerospace systems.

Optimizing turbine blade shape for enhanced efficiency.

ANSYS, Abaqus, MATLAB.

Automobile manufacturers use PDO to enhance vehicle aerodynamics, fuel efficiency, and structural safety.

Optimizing the shape of car spoilers to reduce drag.

CATIA, SolidWorks, COMSOL.

PDO is applied in manufacturing processes to minimize material waste, enhance quality, and reduce costs.

 Optimizing CNC machining parameters (feed rate, cutting speed) for faster production and lower tool wear.

Siemens NX, Mastercam.

Challenges in parametric design optimization

Despite its advantages, PDO presents several challenges,

Computational complexity, as the number of design parameters increases, the optimization process becomes computationally expensive.

Convergence issues, metaheuristic algorithms may struggle to find the global optimum, especially in multi-modal problems.

Data dependency, the quality of optimization depends on accurate input data, which can be difficult to obtain.

Software integration, combining different parametric design and optimization tools can be complex and may require custom scripting.

AI and machine learning (ML) are revolutionizing PDO by accelerating solution search and improving accuracy.

Generative Design, AI-powered tools automatically generate multiple design alternatives based on parametric inputs.

Reinforcement Learning, ML algorithms can adaptively refine design parameters during optimization.

Cloud and parallel computing

Cloud-based optimization platforms allow parallel processing of multiple design iterations, significantly reducing computation time.

Future parametric design frameworks will prioritize sustainability by incorporating lifecycle assessment (LCA) metrics into the optimization process.

Conclusion

Parametric design optimization is transforming the way engineers and designers create products and systems. By leveraging powerful algorithms and computational tools, PDO enables the exploration of innovative, efficient, and sustainable design solutions. The future of PDO lies in the integration of AI, cloud computing, and multi-objective optimization techniques to address increasingly complex design challenges.