• Structural Engineering and Mechanics
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• 제87권3호
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• 한국화재소방학회논문지
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• 제27권3호
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• pp.1-7
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• 2013

전 세계적으로 기후변화 등의 원인으로 산림화재가 대형화되고 있다. 산림이 국토의 약 63.7 %를 차치하며, 산림에 인접해서 산업시설 등의 주요시설들이 많은 국내의 경우에는 산림화재가 대형재난으로 확산될 가능성이 매우 높다. 이에 본 연구에서는 수관화로 발달한 산림화재에서 화염의 복사열이 미치는 피해영향거리를 풍속의 변화에 따라 분석하였다. 또한, 영향거리를 분석하기 위하여 주변시설에 미칠 수 있는 복사열의 안전기준을 조사하였으며, 기준 복사열로 $5kW/m^2$와 $12.5kW/m^2$, $37.5kW/m^2$를 제시하였다. 산림화재 피해영향평가를 위해 FDS의 산림화재 확장 프로그램인 WFDS를 활용하였고, 해석조건으로 국내에 일반적으로 분포가 높은 산림조건에 대해 조사하여 이를 적용하였다. 그 결과는 풍속에 따른 복사열 최대영향거리로 제시하였다. 풍속 0~10 m/s에 있어서 풍속의 증가에 따라 영향거리도 증가하는 경향이 있으며, 풍속 8 m/s에서 영향거리가 최대가 되었다. 또한, 최대 영향거리는 수목 연료의 함수율 증가에 따라 크게 감소하는 것을 확인하였다. 본 연구는 산림화재로 인한 주변시설의 피해영향을 정량적으로 평가하는데 기여할 수 있다.