List of Tables. List of Figures. Introduction. Basic Theory. Moments and Other Expected Values. Linear Estimation Based on Order Statistics. Maximum Likelihood Estimation. Approximate Maximum Likelihood Estimation. Optimal Linear Estimation Based on Selected Order Statistics. Cohen -Whitten Estimators: Using Order Statistics.Estimation in Regression Models. A Sample Completion Technique for Censored Samples. Bibliography. Index.

Order statistics and inference : estimation methods

Computational aspects of biaxial stress in plain and reinforced concrete : proefschrift

Machine learning for structural engineering: A state-of-the-art review

Predicting the compressive strength of concrete containing metakaolin with different properties using ANN

This review article proposes the identification and basic concepts of materials that might be used for the production of high-performance concrete (HPC) and ultra-high-performance concrete (UHPC). Although other reviews have addressed this topic, the present work differs by presenting relevant aspects on possible materials applied in the production of HPC and UHPC. The main innovation of this review article is to identify the perspectives for new materials that can be considered in the production of novel special concretes. After consulting different bibliographic databases, some information related to ordinary Portland cement (OPC), mineral additions, aggregates, and chemical additives used for the production of HPC and UHPC were highlighted. Relevant information on the application of synthetic and natural fibers is also highlighted in association with a cement matrix of HPC and UHPC, forming composites with properties superior to conventional concrete used in civil construction. The article also presents some relevant characteristics for the application of HPC and UHPC produced with alkali-activated cement, an alternative binder to OPC produced through the reaction between two essential components: precursors and activators. Some information about the main types of precursors, subdivided into materials rich in aluminosilicates and rich in calcium, were also highlighted. Finally, suggestions for future work related to the application of HPC and UHPC are highlighted, guiding future research on this topic.

https://www.mdpi.com/1996-1944/14/15/4304/pdf

Materials for Production of High and Ultra-High Performance Concrete: Review and Perspective of Possible Novel Materials.

Search for enhancing the efficiency has led to composite structures such as concrete-filled steel tubes (CFST) with increasing applications across the world. The fire performance of CFST columns needs to be understood comprehensively. In this paper, the effectiveness of the most important parameters on fire resistance rating (FRR) and residual strength index (RSI) was evaluated using artificial neural network (ANN). Relationships were derived from the developed ANN model for predicting the FRR and RSI of CFST columns. Near 300 experimental data points were extracted from the literature and were used for training, validating, and testing the ANN models. The correlation coefficient (R) of the network for FRR and RSI is 0.967 and 0.97, respectively. The derived relationships from the ANN model have the R coefficient of 0.61 and 0.74 for FRR and RSI, respectively. Also, the limitations of existing empirical relationships were compared with the derived relationships. The comparative results indicated that the ANN model was more stable and accurate than the existing relationships. Moreover, a graphical user interface was developed to predict the FRR and RSI of CFST columns. The derived relationships can be used for the design of new CFST columns, retrofit the existing ones, and risk assessment in fires.

The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network

There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test.

Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model

Producing high performance concrete with a low reactivity pozzolan such as metakaolin is challenging. On the other hand, high performance concretes could be more prone to shrinkage cracking due to their higher cement content . No consensus exists in the literature on how metakaolin affects concrete's shrinkage. In this study, the possibility of making high performance concrete with a low reactivity metakaolin was assessed. Moreover, the effect of using a low reactivity metakaolin on the mechanical properties, durability, and free and restrained shrinkage of high performance concrete was evaluated. The results showed that increase in the percentage of metakaolin replacement led to reduction in the mechanical properties and durability of high performance concrete. The effect of metakaolin on shrinkage of high performance concrete was observed to be dependent on the water to cement ratio . Overall, it is possible to produce high performance concrete with a low reactivity metakaolin (up to 20% replacement level) only at low water to cement ratios.

Studying the effect of low reactivity metakaolin on free and restrained shrinkage of high performance concrete

As a lateral load-bearing system, the steel plate shear wall (SPSW) is utilized in different structural systems that are susceptible to seismic risk and because of functional reasons SPSWs may need openings. In this research, the effects of rectangular openings on the lateral load-bearing behavior of the steel shear walls by the finite element method (FEM) is investigated. The results of the FEM are used for the prediction of SPSW behavior using the artificial neural network (ANN). The radial basis function (RBF) network is used to model the effects of the rectangular opening in the SPSW with different plate thicknesses. The results showed that the opening leads to reduced load-bearing capacity, stiffness and absorbed energy, which can be precisely predicted by employing RBF network model. Besides, the suitable relative area of the opening is determined.

Mohammad Javad Moradi

Papers

Predicting the compressive strength of concrete containing metakaolin with different properties using ANN

The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network

Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model

Studying the effect of low reactivity metakaolin on free and restrained shrinkage of high performance concrete

Prediction of the Load-Bearing Behavior of SPSW with Rectangular Opening by RBF Network