A Hybrid Particle Swarm Optimization with Dragonfly for Adaptive ANFIS to Model the Corrosion Rate in Concrete Structures

Khayati, Gholam Reza; Rajabi, Zahra; Ehteshamzadeh, Maryam; Beirami, Hadi

doi:10.1186/s40069-022-00517-9

International Journal of Concrete Structures and Materials

Table 8 The optimal consequent parameters after training.

From: A Hybrid Particle Swarm Optimization with Dragonfly for Adaptive ANFIS to Model the Corrosion Rate in Concrete Structures

Rule numbers	Rules
1	\(\begin{aligned} C_{11(1)} = & - 0.1155 C_{1} - 0.0471C_{2} - 0.0394C_{3} + 0.3621C_{4} - 0.1798C_{5} \\ &\quad + 0.3021C_{6} - 0.0451C_{7} - 0.11C_{8} + 0.0098C_{9} - 3.4646C_{10} + 45.1552 \\ \end{aligned}\)
2	\(\begin{aligned} C_{11(2)} = & - 0.0595 C_{1} - 0.0327 C_{2} - 0.0395 C_{3} + 0.4252 C_{4} - 0.2229C_{5} \\ &\quad + 0.6232 C_{6} - 0.0445 C_{7} - 0.2647C_{8} + 0.01C_{9} - 3.4798 C_{10} + 45.0383 \\ \end{aligned}\)
3	\(\begin{aligned} C_{11(3)} = & - 0.0447 C_{1} - 0.0478 C_{2} - 0.0393 C_{3} + 0.3049 C_{4} - 0.1772C_{5} \\ &\quad + 0.2875 C_{6} - 0.0462C_{7} - 0.1382C_{8} - 0.2404C_{9} - 2.8651 C_{10} + 36.2256 \\ \end{aligned}\)
4	\(\begin{aligned} C_{11(4)} = & - 0.0605 C_{1} - 0.0476 C_{2} - 0.0408 C_{3} + 0.3528 C_{4} - 0.1644 C_{5} \\ &\quad + 0.3031 C_{6} - 0.0433 C_{7} - 0.1854 C_{8} + 0.0116 C_{9} - 3.6469 C_{10} + 45.5615 \\ \end{aligned}\)
5	\(\begin{aligned} C_{11(5)} = & - 0.0613 C_{1} - 1.2013 C_{2} - 0.0007 C_{3} + 0.2849 C_{4} - 0.1646 C_{5} \\ & &\quad + 0.3281 C_{6} - 0.0481 C_{7} - 0.1984 C_{8} + 0.0254 C_{9} - 3.8826 C_{10} + 46.3025 \\ \end{aligned}\)
6	\(\begin{aligned} C_{11(6)} = & - 0.0061 C_{1} - 0.0703 C_{2} - 0.0409 C_{3} + 0.3506 C_{4} - 0.1718_{5} \\ &\quad + 0.3024 C_{6} - 0.0513 C{}_{7} - 0.1488 C_{8} + 0.0112 C_{9} - 3.3384 C_{10} + 44.5699 \\ \end{aligned}\)
7	\(\begin{aligned} C_{11(7)} = & - 0.0614 C_{1} - 0.3094 C_{2} - 0.0384 C_{3} + 0.1063 C_{4} - 0.0015 C_{5} \\ &\quad + 0.3002 C_{6} - 0.0454 C_{7} - 0.1612 C_{8} + 0.0087 C_{9} - 3.7786 C_{10} + 43.5307 \\ \end{aligned}\)
8	\(\begin{aligned} C_{11(8)} = & - 0.0591 C_{1} - 0.1390 C_{2} - 0.0395 C_{3} + 0.3467 C_{4} - 0.1637C_{5} \\ &\quad + 0.3469 C_{6} - 0.0451 C_{7} - 0.1699 C_{8} + 0.0091 C_{9} - 3.5540 C_{10} + 43.3374 \\ \end{aligned}\)
9	\(\begin{aligned} C_{11(9)} = & - 0.0599 C_{1} - 0.0495 C_{2} - 0.0944C_{3} + 0.3234 C_{4} - 0.1250 C_{5} \\ &\quad + 0.2858 C_{6} - 0.0468 C_{7} - 0.0953 C_{8} + 0.0083 C_{9} - 3.5426 C_{10} + 44.1564 \\ \end{aligned}\)
10	\(\begin{aligned} C_{11(10)} = & - 0.0539 C_{1} - 0.0367C_{2} - 0.0383 C_{3} + 0.3950 C_{4} - 0.1626 C_{5} \\ &\quad + 0.3528 C_{6} - 0.0450 C_{7} - 0.1556 C_{8} + 0.0330 C_{9} - 3.6017C_{10} + 50.91 \\ \end{aligned}\)
11	\(\begin{aligned} C_{11(11)} = & - 0.0397C_{1} - 0.0467C_{2} - 0.0194 C_{3} + 0.3538 C_{4} - 0.1689 C_{5} \\ &\quad + 0.2494 C_{6} - 0.0398 C_{7} - 0.1519 C_{8} + 0.0107C_{9} - 3.5988 C_{10} + 39.5035 \\ \end{aligned}\)
12	\(\begin{aligned} C_{11(12)} = & - 0.0721 C_{1} - 0.0497C_{2} - 0.0318 C_{3} + 0.3429 C_{4} - 0.1622 C_{5} \\ &\quad + 0.2888 C_{6} - 0.0822 C_{7} - 0.1854 C_{8} + 0.0070 C_{9} - 3.7603 C_{10} + 44.7857 \\ \end{aligned}\)
13	\(\begin{aligned} C_{11(13)} = & - 0.0592 C_{1} - 0.0453 C_{2} - 0.0401 C_{3} + 0.3538 C_{4} - 0.1591 C_{5} \\ &\quad + 0.2577 C_{6} - 0.0433 C_{7} - 0.2697C_{8} + 0.0147 C_{9} - 3.5458 C_{10} + 40.6151 \\ \end{aligned}\)
14	\(\begin{aligned} C_{11(14)} = & - 0.0605 C_{1} - 0.0494 C_{2} - 0.0410 C_{3} + 0.3982 C_{4} - 0.1391 C_{5} \\ &\quad + 0.3033 C_{6} - 0.0451 C_{7} - 0.1764 C_{8} + 0.0098 C_{9} - 3.5827C_{10} + 42.2834 \\ \end{aligned}\)

Back to article page