Scholarlink

Context <p>Urban areas are increasingly exposed to heat stress, underscoring the need for adaptive, performance-oriented green infrastructure. Tree shading and canopy dynamics play a crucial role in regulating urban microclimates and improving outdoor thermal comfort.</p> Objectives <p>This study aims to develop a generative planting design workflow that optimizes tree configurations across seasons. It integrates vegetation parameters, particularly leaf area index (LAI), into a radiation model and explores trade-offs between thermal comfort and the initial purchase cost of trees.</p> Methods <p>The workflow combines Grasshopper and Ladybug Tools with an evolutionary algorithm as an optimizer to generate planting layouts. Field measurements of mean radiant temperature (MRT) were used to validate solar radiation simulations before optimization.</p> Results <p>The validation improved prediction accuracy in shaded areas by approximately 3 °C. The evolutionary algorithm search revealed multiple planting configurations rather than a single optimal layout. Seasonal differences showed that higher LAI and denser canopies were more effective at summer cooling, whereas sparser structures enhanced winter comfort. Conical-shaped deciduous trees (height = 12 m; trunk diameter = 30 cm) emerged as optimal for both seasons, with LAI values decreasing from 4.6 to 0.6 or 2.8 to 0.0, reflecting seasonal foliage variation. A spacing of 6–12 m further supported adaptability across seasonal contexts.</p> Conclusions <p>The framework integrates an LAI-revised thermal comfort model with a multi-objective NSGA-II genetic algorithm to generate cost-constrained, seasonally adaptive planting schemes that optimize UTCI across summer and winter conditions. The findings provide guidance for climate-responsive, seasonally adaptive tree-planting strategies in urban landscapes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A workflow for climate-adaptive tree planting design to optimize outdoor thermal environment through evolutionary algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

查看全文
  • Zan Peng,
  • Dayi Lai,
  • Bingqin Yu

摘要

Context

Urban areas are increasingly exposed to heat stress, underscoring the need for adaptive, performance-oriented green infrastructure. Tree shading and canopy dynamics play a crucial role in regulating urban microclimates and improving outdoor thermal comfort.

Objectives

This study aims to develop a generative planting design workflow that optimizes tree configurations across seasons. It integrates vegetation parameters, particularly leaf area index (LAI), into a radiation model and explores trade-offs between thermal comfort and the initial purchase cost of trees.

Methods

The workflow combines Grasshopper and Ladybug Tools with an evolutionary algorithm as an optimizer to generate planting layouts. Field measurements of mean radiant temperature (MRT) were used to validate solar radiation simulations before optimization.

Results

The validation improved prediction accuracy in shaded areas by approximately 3 °C. The evolutionary algorithm search revealed multiple planting configurations rather than a single optimal layout. Seasonal differences showed that higher LAI and denser canopies were more effective at summer cooling, whereas sparser structures enhanced winter comfort. Conical-shaped deciduous trees (height = 12 m; trunk diameter = 30 cm) emerged as optimal for both seasons, with LAI values decreasing from 4.6 to 0.6 or 2.8 to 0.0, reflecting seasonal foliage variation. A spacing of 6–12 m further supported adaptability across seasonal contexts.

Conclusions

The framework integrates an LAI-revised thermal comfort model with a multi-objective NSGA-II genetic algorithm to generate cost-constrained, seasonally adaptive planting schemes that optimize UTCI across summer and winter conditions. The findings provide guidance for climate-responsive, seasonally adaptive tree-planting strategies in urban landscapes.