This study systematically examined dredged materials from various aspects, including their sources, the volume generated annually, beneficial uses, and the management processes currently practiced. In addition, this paper presents the relevant policies governing the dredging, reuse, and disposal of dredged materials in the United States. A summary of various sources, types/classifications, and the physical and chemical properties of dredged materials used by various researchers are presented. This paper also summarizes the innovative techniques for the beneficial reuse of dredged materials in a wide range of applications in concrete materials, construction products, roadway construction, habitat building, landfill liner/cap, agriculture soil reconstruction, and beach nourishment. Further, limitations and corresponding solutions related to the beneficial use and management of dredged materials were provided in the end.

Artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR) were developed in this study for predicting resilient modulus (M_r) values of cementitously stabilized subgrade soils. A database was developed using laboratory test results on 160 specimens prepared by using four soils stabilized with three cementitious additives, namely, lime (3%, 6%, 9%), CFA (5%, 10%, 15%), and CKD (5%, 10%, 15%). Of these, 120 specimens (development dataset) prepared using three types of soils were used in development, and the remaining 40 specimens (validation dataset) were used in the validation of the developed models. A commercial software, MATLAB, was leveraged to develop three ANN models (radial basis function network/RBFN; a multilayer perceptrons network/MLPN with one hidden layer; and MLPN with two hidden layers); four SVM models (linear, quadratic, cubic, and radial basis function kernels); and three GPR models (rational quadratic, Matérn, and exponential kernels) by using codes written in MATLAB language. The strengths and weaknesses of the developed models were examined by comparing the predicted M_r values with the observed/experimental values with respect to the mean squared error (MSE) values and determination coefficient (R²) values. Through comprehensive comparison among these three types of models, an MLPN model with one hidden layer was determined as the best performing model developed in this study. It can be used to predict M_r of cementitiously stabilized subgrade soils for Level 2 pavement design applications. This model as well as the other models could be refined using an enriched database.

The structural deterioration knowledge in existing mathematical physics models offers a unique opportunity to develop data-driven, physics-informed machine learning (ML) for enhanced bridge deterioration prediction. However, existing physics models typically represent deterioration knowledge using mathematical symbols and are limited in capturing the semantic meanings and relationships of the symbols, making the models not understandable and operable by machines. As a result, it is challenging to automatically instantiate mathematical physics models to deduce knowledge-driven deterioration facts to inform the learning. To address this challenge, this paper proposes a new structural deterioration knowledge ontology (DT-KL-Onto). The ontology represents structural deterioration knowledge in mathematical physics models as related to structure deficiency, deficiency property, and deficiency contributing factor in a formal, explicit, and semantic way to allow for automated model instantiation. To evaluate the performance of the ontology, the DT-KL-Onto was implemented in automatically instantiating structural deterioration-related mathematical physics models using bridge design, construction, and inspection data. The evaluation results show that the ontology was able to accurately instantiate the models in an automated way: the coefficient of determination and mean squared error between the deterioration facts deduced from the automated instantiation and those from the manual instantiation are 1.0000 and 0.0000, respectively. Two bridge deterioration prediction models were also developed to further demonstrate the benefits of using the deterioration knowledge captured by the ontology to inform the learning. Compared to the purely ML-based prediction model, the physics-informed ML-based prediction model improved the performance of predicting the condition ratings of concrete decks by 10.0%. This research contributes to the body of knowledge by offering an ontology that can semantically represent structural deterioration knowledge in mathematical physics models to enable automated model instantiation. This research thus has the potential to fully unleash the power of the large amount of bridge data and deterioration knowledge that exist to develop next-generation learning machines for data-driven, physics-informed ML-based deterioration prediction and maintenance decision making.