隨機森林與機器學習方法預測能力比較-以混凝土抗壓能力為例

Abstract

本研究旨在探討隨機森林與其他機器學習方法在預測混凝土抗壓能力方面的相對效能。混凝土抗壓能力是評估混凝土品質和結構強度的重要指標。研究以隨機森林為主要預測模型，並與其他機器學習方法進行對比，包括梯度提升技術、決策樹等。在研究中，我們首先收集了具有多樣性混凝土特性的數據集，包括不同成分、製程和時間點的數據。接著，我們利用這些數據進行模型訓練和測試，評估各模型在預測混凝土抗壓能力方面的性能。隨機森林模型以其擬合能力和抗過擬合特性而聞名，我們將進一步分析其與其他方法的比較優勢。研究結果將有助於深入了解隨機森林在混凝土抗壓能力預測中的表現，並提供選擇最適合的模型以改進混凝土品質預測的參考。這項研究對於擴展機器學習在建築材料領域的應用具有重要意義。This study aims to explore the relative performance of Random Forest and other machine learning methods in predicting the compressive strength of concrete. Compressive strength is a crucial indicator for assessing concrete quality and structural integrity. The study focuses on Random Forest as the primary predictive model and compares it with other machine learning methods, including Support Vector Machines, Decision Trees, and others.In this research, a diverse dataset with varied concrete properties, including different compositions, processes, and time points, was collected. Subsequently, the data were utilized for model training and testing to evaluate the performance of each model in predicting concrete compressive strength. Known for its fitting ability and resistance to overfitting, the Random Forest model will be further analyzed for its comparative advantages over other methods.The findings of this research will contribute to a deeper understanding of the performance of Random Forest in predicting concrete compressive strength and provide insights into choosing the most suitable model to enhance predictions of concrete quality. This study holds significance in extending the application of machine learning in the field of construction materials.