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• A new PINN-based topology optimization framework is proposed. • The proposed framework employs PINN to conduct structural analysis to replace FEA. • The proposed framework does not require any labelled data to train PINN. • The proposed framework can effectively optimize the problem domains. • Comparisons and discussions are provided between the topology optimization methods. Physics-Informed Neural Networks (PINNs) have recently attracted exponentially increasing attention in the field of computational mechanics. This paper proposes a novel topology optimization framework: Physics-Informed Neural Network-based Topology Optimization (PINNTO). Unlike existing machine-learning based topology optimization frameworks, PINNTO employs an energy-based PINN to replace Finite Element Analysis (FEA) in the conventional structural topology optimization, to numerically determine the deformation states, which is a key novelty in the proposed methodology. A supervised neural network that respects governing physical laws defined via partial differential equations is trained to develop the corresponding network without any labelled data, with the intention of solving solid mechanics problems. To assess feasibility and potential of the proposed PINNTO framework, a number of topology-optimization-related case studies have been implemented. The subsequent findings illustrate that PINNTO has the ability to attain optimized topologies with neither labelled data nor FEA. In addition, it has the capability to generate comparable designs to those produced by the current successful approaches such as Solid Isotropic Material with Penalization (SIMP). Based on the results of this study, it can also be deduced that PINNTO can acquire optimal topologies for various types of complex domains given that the boundary conditions and loading configurations are correctly imposed for the associated energy-based PINN. Consequently, the proposed PINNTO framework has demonstrated promising capabilities to solve problems under conditions when the usage of FEA is challenged (if not impossible). In summary, the proposed PINNTO framework opens up a new avenue for structural design in this ' data-rich ' age. [ABSTRACT FROM AUTHOR]

The degree of compaction in the levee building materials is a crucial factor that affects the piping phenomena. The density and compaction of the soil strata determine the structural soundness of the levee. Segments with reduced density or compaction can become weak spots during floods. To assess part of the Helena levee (2,500 m) in Arkansas (AR), the United States, an extensive ground-penetrating radar (GPR) fieldwork was conducted. This reliable method will undoubtedly improve the assessment procedure of the levee structure by identifying the weak spots within the structure that result from poor compaction of the levee core layers in a short time with high accuracy. However, interpreting the GPR data can be challenging and requires specialised knowledge. Obtaining meaningful insights typically involves a time-consuming process of extensive manual processing and visual inspection. To address this issue, this article proposes a novel, reliable deep-feature fusion framework for GPR data to identify horizontal variation in the soil compaction of a levee. To address data scarcity, a new type of transfer learning in the same domain is adopted, and four deep learning models (Xception, Inception, EfficientNet and MobileNet) are used to extract features. The combined features are then used to train and test five machine learning classifiers (Neural Network, Support Vector Machine, K-Nearest Neighbour, Logistic Regression, and Naive Bayes). The best combination of deep Learning and machine learning is four models with the neural network classifier which achieved the highest results by obtaining an accuracy of 98.2%, an F1 score of 97.6%, and an area under the curve of 99.9%. The proposed framework faced an additional challenge when subjected to an unseen dataset of 1,511 images reserved primarily for testing. Remarkably, it achieved an accuracy rate of 95.7% with the neural network classifier. This article presents a new research direction that has substantial potential in various domains, including civil engineering, the petroleum sector, road safety, agriculture, and more. • A Reliable deep feature fusion technique for the Ground Penetrating Radar (GPR) Data. • A new type of transfer learning is adopted to address training data scarcity issues. • Feature fusion is performed on four DL models. • Five machine learning classifiers have been employed. • The proposed framework achieved an accuracy of 98.2%, an F1 score of 97.6% and an AUC of 99.9 %. [ABSTRACT FROM AUTHOR]

Polymer nanocomposites with excellent mechanical performances have been increasingly sought after in engineering applications such as biotechnology, aerospace, and automotive areas. Through molecular dynamics (MD) simulation, this work systematically assessed the tensile performance of poly(methyl methacrylate) (PMMA) nanocomposite reinforced by randomly dispersed two-dimensional diamond-diamane. It is found that randomly dispersed diamane effectively enhances the tensile properties of PMMA with surface functionalization, and the enhancement effect can be remarkably augmented by cross-linking. Simulations reveal that the enhancement effect can be effectively tailored by the alignment of the diamane fillers. The PMMA nanocomposites exhibit much better tensile performance when the diamane fillers are uniformly aligned along the in-plane direction of the filler. Additional investigations show that larger diamane filler is preferred without cross-linking, while smaller diamane filler should be considered when cross-linking is present. Overall, the impacts of different factors on the tensile properties of PMMA nanocomposites are analysed in-depth in this work, which provides atomistic insights for the preparation of polymer nanocomposites with desired mechanical properties. Effective enhancement on the tensile properties of polymer nanocomposites can be achieved by 2D diamond nanofiller. [Display omitted] [ABSTRACT FROM AUTHOR]