following is a general tensorflow keras code and nothing fancy but will work for many applications, have to adapt it for your suiting while processing is the key for better output;

import numpy as np

import pandas as pd

from keras.models import Sequential

from keras.layers import Dense

x = #load data

y = #load data

# do the pre processing accordingly *essential step

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

#Define

model = Sequential()

model.add(Dense(10, input_dim=x.shape[1], activation='relu'))

model.add(Dense(10, input_dim=x.shape[1], activation='relu'))

model.add(Dense(1, activation='linear'))

#Compile

model.compile(loss='mean_squared_error', optimizer='adam')

#Train

model.fit(x_train, y_train, epochs=10, batch_size=32)

#Test

loss = model.evaluate(x_test, y_test)

print('Test loss:', loss)

#Ptredict

x_new = #load data

predictions = model.predict(x_new)

print('Predictions:', predictions)

import numpy as np

from sklearn.neural_network import MLPRegressor

# Load the data from a file or other source

X = ... # input features (e.g., thickness of steel layer, concrete strength, etc.)

y = ... # target strength values

# Split the data into training and test sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Standardize the data (optional)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

# Define the model

model = MLPRegressor(hidden_layer_sizes=(100, 50, 25), max_iter=1000)

# Train the model

model.fit(X_train, y_train)

# Evaluate the model on the test set

score = model.score(X_test, y_test)

# Print the R^2 score

print(f'Test R^2 score: {score:.3f}')