인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The construction industry continuously seeks innovative materials and methodologies to enhance structural integrity while minimizing environmental impact. This study investigates the predictive capabilities of Artificial Neural Networks (ANN) in estimating the compressive strength of clay brick. Employing a dataset derived from comprehensive experimental trials encompassing varying compositions and curing conditions, an ANN model was developed and trained to predict the compressive strength of glass wool reinforced composite bricks. The inputs to the ANN comprised key parameters including the proportions of glass wool content, load at failure, area of cross-section and burning temperature. The model was optimized through iterative training processes to attain robustness and accuracy in predicting compressive strength. Subsequently, validation was performed using separate test datasets to evaluate the model’s generalization capacity. The results demonstrate the efficacy of the ANN model in accurately forecasting the compressive strength of glass wool reinforced clay brick. The analysis reveals nuanced correlations between glass wool content, load at failure, area of cross-section and burning temperature, and the resultant strength, shedding light on the intricate dynamics governing these composite materials. This ANN-based predictive approach presents a useful tool for engineers and stakeholders in the construction industry to anticipate and optimize the compressive strength of glass wool reinforced clay bricks. Furthermore, the findings contribute to advancing the understanding of these novel composite materials, fostering sustainable and resilient construction practices.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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