Applicable Artificial Neural Network Modeling For ready mix Concrete industry

摘要 混凝土是營建工程的常見材料，其中抗壓強度是混凝土品質評估的重要指標之一，而坍度則是現場工作性的重要指標。過去研究使用不同方法針對材料配比所產生的抗壓強度、坍度製作預估模型，其中最常見也具高準確率的是類神經網路技術。 然而前人之研究僅考量混凝土配比設計之材料用量之自變數(如水灰比、用水量、水泥用量、細粒料用量、粗粒料用量及飛灰用量 )，即有準確率高的效果。但未考量混凝土材料性質變數對混凝土抗壓強度造成之影響，故本研究嘗試加入混凝土材料性質之新增變數，以探討其影響程度。 為建立一通用模型，本研究收集業界不同來源之混凝土抗壓強度、坍度數據共600筆，並提出其他9項自變數：粗骨材之最大粒徑、粗骨材之磨損率、粗骨材之面乾比重、粗骨材之吸水率、粗骨材之乾搗單位重、細骨材之細度模數、細骨材之75μm含量、細骨材之面乾比重、細骨材之吸水率，冀望這些新變數能反應不同料源的材料特性。 案例成果說明 6 項舊變數所建立之配比模型，其 RMSE 誤差值皆高於 15 項自變數模型的誤差值。多考慮 9 項新變數的確有助於抗壓強度、坍度的預估準確度，顯示不同料源之材料特性可以此 15 項自變數表示之。摘要 -----------------------------------------------------------------i 目錄---------------------------------------------------------------iii 表目錄--------------------------------------------------------------iv 圖目錄---------------------------------------------------------------v 第一章 緒論 1 一、研究動機 1 二、研究方法 1 第二章 文獻回顧 2 一、類神經網路概述 2 二、混凝土概述 6 第三章 規劃與測試 19 一、類神經網路系統測試軟體 19 二、建構類神經網路推估混凝土強度模式 19 第四章 結論與建議 21 一、結論 21 二、建議 21 參考文獻 22 附錄A混凝土配比設計、抗壓強度及坍度資料表 24 附錄B混凝土28天抗壓強度及坍度TRAINNING與TEST測試結果表 56 附錄C混凝土28天抗壓強度及坍度X1-X15與X10-X15 TEST測試結果比較圖 68 附錄D混凝土28天抗壓強度及坍度TRAINNING與TEST比較圖 69 附錄E粗骨材最大粒徑9項新變數直方圖 71 附錄F粗骨材最大粒徑9項新變數散佈圖 7

Experimental cross-contamination of chicken salad with Salmonella enterica serovars Typhimurium and London during food preparation in Cambodian households

Non-typhoidal Salmonellae are common foodborne pathogens that can cause gastroenteritis and other illnesses in people. This is the first study to assess the transfer of Salmonella enterica from raw chicken carcasses to ready-to-eat chicken salad in Cambodia. Twelve focus group discussions in four Cambodian provinces collected information on typical household ways of preparing salad. The results informed four laboratory experiments that mimicked household practices, using chicken carcasses inoculated with Salmonella. We developed four scenarios encompassing the range of practices, varying by order of washing (chicken or vegetables first) and change of chopping utensils (same utensils or different). Even though raw carcasses were washed twice, Salmonella was isolated from 32 out of 36 chicken samples (88.9%, 95% CI: 73.0–96.4) and two out of 18 vegetable samples (11.1%, 95% CI: 1.9–36.1). Salmonella was detected on cutting boards (66.7%), knives (50.0%) and hands (22.2%) after one wash; cross-contamination was significantly higher on cutting boards than on knives or hands (p-value < 0.05). The ready-to-eat chicken salad was contaminated in scenario 1 (wash vegetables first, use same utensils), 2 (wash vegetables first, use different utensils) and 3 (wash chicken first, use same utensils) but not 4 (wash chicken first, use different utensils) (77.8%, 11.1%, 22.2% and 0%, respectively). There was significantly higher Salmonella cross-contamination in scenario 1 (wash vegetables first, use same utensils) than in the other three scenarios. These results show how different hygiene practices influence the risk of pathogens contaminating chicken salad. This information could decrease the risk of foodborne disease in Cambodia and provides inputs to a quantitative risk assessment model