Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model.

In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress-strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method.

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm.

There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

Variation Pattern of the Elastic Modulus of Concrete under Combined Humidity and Heat Conditions.

The coupling effect of moisture content and temperature on the elastic modulus of concrete is experimentally investigated. The elastic modulus of dry concrete exhibits a clear temperature-weakening effect, while the elastic modulus of wet concrete exhibits a water-strengthening effect at room temperature. Under humidity-heat conditions, the elastic modulus of wet concrete declines with the temperature rise. When the temperature is 20 °C, 200 °C, 400 °C, 520 °C, and 620 °C, the humidity-heat coupling factors of the elastic modulus change rate DI˙F with moisture content are 0.08, 0.07, 0.04, 0.01, and -0.03, respectively, and the declining rate increases with the rise of moisture content. The relation between the humidity-heat coupling factor DIF, moisture content, and temperature was established; The equivalent relation between the water-strengthening effect and the temperature-weakening effect of the elastic modulus was obtained. The temperature range of the strengthening effect and "apparent weakening effect" of water stored inside concrete before heating on elastic modulus was determined; The evolutionary mechanism of the competition between the microcrack expansion and healing of concrete under combined humidity and heat conditions was revealed.

A 21­gene Support Vector Machine classifier and a 10­gene risk score system constructed for patients with gastric cancer.

Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a Support Vector Machine (SVM) classifier and risk score system for GC. The GSE62254 (training set) and GSE26253 (validation set 2) datasets were downloaded from the Gene Expression Omnibus database. Furthermore, the gene expression profile of GC (validation set 1) was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) between recurrent and non­recurrent samples were determined using the limma package. The feature genes were selected using the Caret package, and an SVM classifier was built using the e1071 package. Using the penalized package, the optimal predictive genes for constructing a risk score system were screened. Finally, stratification analysis of clinical factors and pathway enrichment analysis were performed using Gene Set Enrichment Analysis. A total of 239 DEGs were identified in GSE62254, among which 114 DEGs were significantly associated with both recurrence­free survival and overall survival. Subsequently, 21 feature genes were screened from the 114 DEGs, and an SVM classifier was built. A risk score system for survival prediction was constructed, following the selection of 10 optimal genes, including A­kinase anchoring protein 12, angiopoietin­like protein 1, cysteine­rich sequence 1, myeloid/lymphoid or mixed­lineage leukemia, translocated to chromosome 11, neuron navigator 3, neurobeachin, nephroblastoma overexpressed, pleiotrophin, tumor suppressor candidate 3 and zinc finger and SCAN domain containing 18. The stratification analysis revealed that pathological stage was an independent prognostic clinical factor in the high­risk group. Additionally, eight significant pathways were associated with the 10­gene signature. The SVM classifier and risk score system may be applied for classifying and predicting the prognosis of patients with GC, respectively.

A 65­gene signature for prognostic prediction in colon adenocarcinoma.

The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival­related genes were selected from the DEGs using the Cox regression method. A co­expression network of survival­related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan­Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival­related genes selected. The co­expression network of survival­related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator­activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine­cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65­gene signature was established using this co­expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e­12) and the GSE17538 dataset (P=1.67e­6). The 65­gene signature included kallikrein­related peptidase 6 (KLK6), collagen type XI α1 (COL11A1), cartilage oligomeric matrix protein, wingless­type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65­gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.