Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

Abstract

Stabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building construction, and highway construction. Using the traditional method such as empirical approach and experimental methods, the determination of compressive strength of stabilized dredged sediments is difficult due to the complexity of this composite material. In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. To perform the simulation, 51 experimental datasets were collected from the literature. The dataset consists of 4 input variables (water content, cement content, air foam content, and waste fishing net content) and output variable (compressive strength). Evaluation of the models was made and compared on training dataset (70% data) and testing dataset (30% remaining data) by the criteria of Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. The cement content is the most important input affecting the unconfined compressive strength. The important input affecting the unconfined compressive strength can be in the following order: cement content > air foam content > water content > waste fishing net.

Results of Application of Artificial Neural Networks in Predicting Geo-Mechanical Properties of Stabilised Clays—A Review

“…From the study, it was discovered that most of the ANN models utilized were backpropagation feed-forward networks with one hidden layer. Only two studies [56,58] used two hidden layers for the analysis. Other studies have also pointed out that most regression analyses in soil stabilisation problems are easily solved using a simple architecture of one hidden layer.…”

“…Performance evaluation of the devel- To alleviate the difficulties associated with the need for continuous experimental determination of UCS, ref. [58] employed machine learning in analysing and modelling the performance of stabilised dredged sediments. A total of 51 experimental datasets were collated from existing literature for the development of the ANN model.…”

Prediction of California Bearing Ratio (CBR) of Stabilized Expansive Soils with Agricultural and Industrial Waste Using Light Gradient Boosting Machine

“…In this process, three performance criteria were used namely correlation coefficients R, root mean square error RMSE and mean absolute error MAE to assess the accuracy of LighGBM model [7]:…”

“…Especially, the ML model has high generality and accuracy when the model uses large samples in training the model. Therefore, ML models have been applied to solve many problems in civil engineering such as determination of pile bearing capacity [5], [6], unconfined compressive strength of stabilized soil [7], compressive strength of concrete [8], [9], etc. Therefore, the ML models have been developed in determining the CBR of stabilized expansive soils.…”

Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials

“…Machine learning predictions would help design such complex materials. Indeed, artificial intelligence techniques have been successfully applied to several Civil Engineering problems such as concrete strength prediction [40,41], creep prediction [42][43][44], crack assessment in structures [45] or durability and microstructural properties such as surface chloride concentration [46] and and mechanical properties of stabilized soil [47,48]. Among the various techniques developed, ensemble machine learning algorithms applied to datasets with hundreds of data points have proved a good accuracy and robustness against overfitting risk, often associated to conventional techniques and neural networks.…”