Nowadays, various construction site monitoring (CSM) models have been presented using sound signals. Many researchers have used deep learning (DL) networks to develop an accurate automated CSM model. These DL-based models require huge dataset to train the model and also such networks are complex. Hence, in this work, a novel hand-modeled automated system is developed using a public CSM sound dataset. The proposed model uses the first S-Box of the data encryption standard (DES) cipher as a feature generator by using two binary kernels. Using tent average pooling, sub-bands (compressed) sound signals are generated and the presented multiple kernelled DES pattern generates features from each signal. The proposed hand-modeled automated system extracts 25 feature vectors, hence it is named as DesPatNet25. The developed DesPatNet25 consists of: (i) feature vectors creation, (ii) feature selection using iterative neighborhood component analysis (INCA), and (iii) classification. Our proposed model attained accuracies of 96.77% and 97.05% using k-nearest neighbor (kNN) classifier with 10-fold cross-validation and hold-out validation (80:20 split ratio) techniques, respectively. These high classification accuracies clearly demonstrate the success of the DesPatNet25 model with sound signal classification for automated CSM tasks.