{'fraction_non_alphanumeric': 0.028594771241830064, 'fraction_numerical': 0.016339869281045753, 'mean_word_length': 5.186868686868687, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A novel strength behavior prediction model of self‐compacting concrete with foundry sand and hybrid fibers

In this paper, an Improved FireFly Optimization Algorithm (IFFOA)‐based optimal Self‐Organizing Polynomial Neural Network (SOPNN) is proposed to analyze the compressive strength, tensile strength, and flexural strength of the self‐compacting concrete (SCC) with the inclusion of hybrid fibers whenever exposed to high temperature. The SCC mixture is formed by the materials taken in this study along with the hybrid fibers. The preparation of SCC is made by adding water to powder with a proportion of 3:10 and hybrid fibers content is about 2 kg/m3. The samples are heated to a temperature of 210, 320, 530, and 790° at the duration of 7 and 28 days. Experiments are performed to analyze the loss in compressive strength, tensile strength, and flexural strength. The proposed method is used to analyze the loss functions based on the amount of cement, aggregates, temperature, additives along with the inclusion, and exclusion of hybrid fibers. Further, it is found the proposed method analyzes the loss in compressive strength, tensile strength, and flexural strength of SCC more accurately than the other methods.