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  • Writer's pictureAdisorn O.

Generation of Tendon Profile under user's Control Parameters

Updated: Jun 15, 2023


In most metaheuristic algorithms like Genetic Algorithm, Simulated Annealing, or particle swarm optimization, it is required to generate random variables under some control parameters. For example, we can generate a sample of z value controlled by % load balancing and its boundary limits using this algorithm:


z1 = z got from % Maximum load balancing, i.e., 120%;

z2 = z got from % Minimum load balancing, i.e., 70%;

To compute z1 and z2, a particular function is required to convert % load balancing and drape value into z value. for example


MATLAB CODE:

%

% compute z from given balance load

% use for generating valid chromosome

%

function o = z_from_wbal (w_bal,F,Area,h,zend,ispan)

global L b bw

num_spans = length(L);

drape = w_bal/100*L(ispan)^2*24*Area/8/F;

if ispan == 1 || ispan == num_spans

z = 1/2*(zend+h-0.05)-drape;

else

z = h-0.05-drape;

end

o = z;

end


Then compare z with its boundary values.

% bound of z from wbmin, wbmax, lb_z, ub_z

z_low = max([lb_z, z_from_wbal(wbmax,F,e(i),Area,h,zend,i)]);

z_high = min([ub_z, z_from_wbal(wbmin,F,e(i),Area,h,zend,i)]);

z(i) = max([z_low+(z_high-z_low)*rand,lb_z]);

disp([z_low,z_high,z(i)])



The result of z is guaranteed to lie within the boundary between wb_max and wb_min, but in extreme cases, they are also controlled within the [zmin, zmax] range.


In PT-OP, software for design optimization of PT slab developed for Posteck Prestressing, we employ the above method to control the depth of tendon z, so that the % balance load stays within the control parameters.



Figure Percentage of Balance Load control [70%, 120%] in PT-OP

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