混凝土為目前世界上用量最多、用途最廣的建築材料之一，其力學性質中抗壓強度至關重要。當前規範以28天標準養護後的混凝土抗壓實驗為標準的測試方法，需等混凝土硬化並形成強度後進行抗壓強度試驗，方能檢視材料配比設計或養護環境場域的適宜性。理想的情況為實際強度恰等於目標強度，故若能提早預估需經後驗方能得知的抗壓強度，並提升預測模型準確性，將有助於混凝土強度品質管理，達安全且經濟之優勢。混凝土配比設計中，水膠比為決定混凝土強度的最主要因素，然不同的預拌廠雖採相同配比，卻常產出不同強度的預拌混凝土，由此可知混凝土強度仍受其它因素，諸如水泥、粗細粒料含量、飛灰爐石比例、坍度等因素之影響。由於基本的統計方法未能有效反應預拌廠混凝土強度及相關組成材料間可能存在的非線性函數關係，本研究將探討實務累積的抽驗資料態樣，運用人工智慧技術及啟發式優化演算法，建構最佳化機器學習模型，茲以預測預拌廠生產的混凝土抗壓強度，供相關單位進行先期品質管制。經回顧文獻常用之演算法，單一模型如人工神經網絡、支援向量機、決策樹、線性回歸；複合模型為表決法、重複採樣平均表決法、演化堆疊法，以及時下具良好評價的極限梯度提升法(XGBoost)。基於上述人工智慧模型進行效能評估，確立最合適之機器學習模型為XGBoost，接續結合自行開發的鑑識科學流程萬用啟發式演算法(Forensic-Based Investigation algorithm, FBI)，調教具最佳效能的極限梯度提升法模型，優化最終預測模型的泛化能力。最後，以該預測模式為核心，開發預拌混凝土強度預測系統介面，便利現地品質工程師操作及紀錄。

Concrete is currently one of the most widely adopted building materials in the world. The compressive strength of concrete is important for its mechanical properties. The current specification applies the 28-day curing standard concrete compression test as the official test method. To check either the designing of material ratio or suitability of the curing environment, the sample of concrete must be cured and hardened before performing the compressive strength test. Enhancing the precision of the prediction model improve the quality control process and achieve the safety and economic benefits. The most important factor I the concrete mix ratio is the water-binder, which determines the strength of the concrete. Nevertheless, other factors, could be cement, coarse, fine aggregate, fly ash, furnace stone or slump, may result in the same design leading to different outcomes from ready-mix concrete plants. However, the basic statistical methods fail to show the non-linear functional relationship between the concrete strength of the ready-mix plant and the compounds of concrete. This study examines the patterns of accumulated sampling data using artificial intelligence techniques and heuristic optimization algorithms to construct an optimized machine learning model for predicting the compressive strength of concrete produced by a ready-mix plant for pre-controlling of quality. Many common algorithms are found in previous literature. Methods based on single models include artificial neural networks, support vector machines, regression trees and linear regression. Ensemble models include voting, bagging, stacking and Extreme Gradient Boosting. This investigation selects the best model by evaluating the performance of the above models, and combining the Forensic-Based Investigation algorithm (FBI) developed by our lab to enhance the hyper-parameter of the Extreme Gradient Boosting and to optimize the generalization ability of predictive models. Finally, an interface is developed for the concrete strength prediction system to enable quality control engineers to operate and record data using the proposed predicting model.

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