Prediction of the Normalized COVID-19 Epidemic Prevention Costs of Construction Projects Based on an Optimized Neural Network

Algorithm 1

Algorithm of the proposed model.

	Input: Data collected D (a, b)
	Output: Trained models
	h = generate h groups of different choices
	u = number of outliers deleted
	m = first deleted outlier sequence number
	Step 1: Data normalization processing
	Step 2: Training of models with normalized data
	for  = 1 to h
	for every D (, m)
	do
	delete the m-th data
	data D (,:) import into neural network
	Output: Output fitting results
	m = m+1
	while (Calculate all values)
	end
	Step 3: Calculate the average avg of h groups of data
	Step 4: Pick out the top 5% and remove
	Step 5: Import remaining data into neural network
	Step 6: Execute genetic algorithm
	Genetic algorithm coding
	do
	Genetic algorithm crossover
	Genetic algorithm variation
	Genetic algorithm selection
	While (the maximum number of iteration is reached!)
	Output: weight and threshold
	Step 7: training BP neural network
	Output: final result