18 - Expression Programming Techniques for Formulation of Structural Engineering Systems

An equivalent stiffness approach for modelling the behaviour of compression members according to Eurocode 3

An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming

Evolutionary Symbolic Models for Predicting Mortar Strength Incorporating Plastic Waste and Supplementary Cementitious Materials for Sustainable Construction

Cementitious composites' performance degrades in extreme conditions, making it more important to enhance its resilience. To further the adaptability of eco-friendly construction, waste materials are increasingly being repurposed. Cementitious composites deteriorate in both direct and indirect ways due to the facilitation of hostile ion transport by water. The effects of using eggshell and glass powder as partial substitutes for cement and sand in mortar on the water-absorption capacity were investigated using machine learning (ML) modeling techniques such as Gene Expression Programming (GEP) and Multi Expression Programming (MEP). To further assess the importance of inputs, sensitivity analysis and interaction research were carried out. The water absorption property of cementitious composites was precisely estimated by the generated ML models. It was noted that the MEP model, with an R2 of 0.90, and the GEP model, with an R2 of 0.88, accurately predicted results. The sensitivity analysis revealed that the absorption capacity of the mortar was most affected by the presence of eggshell powder, sand, and glass powder. GEP and MEP model's significance lies in the fact that they offer one-of-a-kind mathematical formulas that can be applied to the prediction of features in another database. The mathematical models resulting from this study can help scientists and engineers rapidly assess, enhance, and rationalize mixture proportioning. The built models can theoretically compute the water absorption of cement mortar made from eggshell powder and glass powder based on varied input parameters, resulting in cost and time savings.

Background: Sandwich panels with low density core constructions are a class of structural elements with superior structural performance compared to traditional solid panels. Sandwich panels, compared to solid panels of equal mass, generally have much higher bending stiffness and comparable stretching stiffness, thus undergoing smaller deformation under loading. The potential advantage of sandwich panels for mitigating shock and projectile loading has been established in literature. The previous studies related to understanding the mechanics and structural performance of sandwich structures are mainly focused on studying the behavior of the core construction or the performance of a single isolated sandwich panel. These studies have provided significant insight into the behavior of sandwich panels, which includes mechanisms of energy dissipation in the core, stages of deformation of sandwich panels as they get impinged by an intense shock, fluid-structure effects for shock waves transmitted in air and water, and the mechanism of deformation and failure of sandwich panels under shock and projectile loading. Objective: This study extends previous research by exploring the behavior and structural performance of structural systems made of sandwich walled panels. The objective is to explore the potential benefit of sandwich configurations in enhancing the overall behavior of structural systems under complex loading conditions that could occur in petrochemical industry settings and pipeline networks. Method: We considered three frame structures, (i) frames made of solid panels (ii) frames with sandwich panel side walls and (iii) frames where all walls are made of sandwich panels. We also considered sandwich walled cylinders resembling pipes and compared their performance with the mechanical behavior of traditional pipes made of solid metal hollow cylinders. We used detailed finite element models to simulate the response of these structural systems. Results: The results showed that sandwich walled frame and cylinder (pipeline) configurations, in general, undergo smaller deflection than traditional counterpart structures under both quasi-static and high intensity dynamic (shock) loadings. Conclusions: The results highlight the potential of sandwich walled structural systems for developing novel threat resistant structures especially for the petrochemical industry and pipeline networks.

Geopolymer concrete (GPC) serves as a sustainable substitute for conventional concrete by employing alternative cementitious materials such as fly ash (FA) instead of ordinary Portland cement (OPC), contributing to environmental and durability benefits. To increase the rate of utilization of FA in the construction industry, distinctive characteristics of two machine learning (ML) methods, namely, gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to propose precise prediction models for the compressive strength and split tensile strength of GPC comprising FA as a binder. A comprehensive database was collated, which comprised 301 compressive strength and 96 split tensile strength results. Seven distinct input variables were employed for the modeling purpose, i.e., FA, sodium hydroxide, sodium silicate, water, superplasticizer, and fine and coarse aggregates contents. The performance of the developed models was assessed via numerous statistical metrics and absolute error plots. In addition, a parametric analysis of the finalized models was performed to validate the prediction ability and accuracy of the finalized models. The GEP-based prediction models exhibited better performance, accuracy, and generalization capability compared with the MEP-based models in this study. The GEP-based models demonstrated higher correlation coefficients (R) for predicting the compressive and split tensile strengths, with the values of 0.89 and 0.87, respectively, compared with the MEP-based models, which yielded the R values of 0.76 and 0.73, respectively. The mean absolute errors for the GEP- and MEP-based models for predicting the compressive strength were 5.09 MPa and 6.78 MPa, respectively, while those for the split tensile strengths were 0.42 MPa and 0.51 MPa, respectively. The finalized models offered simple mathematical formulations using the GEP and Python code-based formulations from MEP for predicting the compressive and tensile strengths of GPC. The developed models indicated practical application potential in optimizing geopolymer mix designs. This research work contributes to the ongoing efforts in advancing ML applications in the construction industry, highlighting the importance of sustainable materials for the future.

Transcription and translation are required for consolidation of long-lasting changes in synaptic function and are required for learning and memory. The targets of activity-dependent transcription in neurons have been of great interest. Despite this, the ultimate consequences of an activity-dependent change in programs of gene expression with respect to neural function have been surprisingly elusive. For example, experimental data have not clearly established how gene expression is required for memory-associated events, such as synapse-specific strengthening or changes in the input-output function of the neuron. Many activity-regulated genes are transcriptional factors which themselves modify programs of downstream gene expression, and the short- and long-term consequences of these changes in gene expression remain largely unknown. Although there have been many complementary approaches to experimentally and computationally define the plasticity transcriptome, the specific gene targets that have been identified using different experimental approaches show surprisingly little overlap. The purpose of this review is three-fold: 1) to discuss what is known about activity-dependent transcription during learning, 2) to review efforts to identify genes in the plasticity transcriptome, and 3) to develop a hypothesis about how transcription might be required for information coding at the synaptic and cellular level.

This book gives a comprehensive introduction to computer algebra together with advanced topics in this field. It provides a detailed coverage of the mathematics of computer algebra as well as a step-by-step guide to implement a computer algebra system in the object-oriented language C++. The used tools from C++ are introduced in detail. Numerous examples from mathematics, physics and engineering are presented to illustrate the system's capabilities. Computer algebra implementations in LISP and Haskell are also included. In addition, gene expression programming and multiexpression programming with applications to computer algebra are introduced.

This study presents a novel data-driven approach to improving the compressive strength (C-S) of environmentally friendly rubberized mortar that incorporates ingredients that are in line with current sustainability objectives in construction: glass powder, marble powder, and silica fume. Our predictive models were built using state-of-the-art machine learning (ML) approaches, specifically gene expression programming (GEP) and multi-expression programming (MEP), employing a thorough experimental dataset. Thorough evaluations of the models were conducted using important statistical metrics, such as the R 2 coefficient, root mean square error, and mean absolute error. The use of individual conditional expectation plots and partial dependence plots allowed for both individual and average variable effect studies, which were conducted to improve interpretability. Despite the good performance of the GEP model ( R 2 = 0.91), the MEP model proved to be more effective in capturing complicated, nonlinear connections with its superior accuracy and generalization ( R 2 = 0.95). ML has the ability to greatly improve sustainable construction practices by reducing the need for experiments, speeding up the process of mix optimization, and encouraging the creation of cementitious composites that are less harmful to the environment. The findings contribute to the construction sector by integrating digital innovation with material sustainability.

Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

Proposition of an equivalent reduced thickness for composite steel plate shear walls containing an opening

In this chapter it is shown how Automatically Defined Functions are encoded in the genotype/phenotype system of Gene Expression Programming. As an introduction, the fundamental differences between Gene Expression Programming and its predecessors, Genetic Algorithms and Genetic Programming, are briefly summarized so that the evolutionary advantages of Gene Expression Programming are better understood. The introduction proceeds with a detailed description of the architecture of the main players of Gene Expression Programming (chromosomes and expression trees), focusing mainly on the interactions between them and how the simple, yet revolutionary, structure of the chromosomes allows the efficient, unconstrained exploration of the search space. The work proceeds with an introduction to Automatically Defined Functions and how they are implemented in Gene Expression Programming. Furthermore, the importance of Automatically Defined Functions in Evolutionary Computation is thoroughly analyzed by comparing the performance of sophisticated learning systems with Automatically Defined Functions with much simpler ones on the sextic polynomial problem. 1. Genetic Algorithms: Historical Background The way nature solves problems and creates complexity has inspired scientists to create artificial systems that learn how to solve a particular problem without human intervention. The first attempts were done in the 1950s by Friedberg (Friedberg 1958; Friedberg et al. 1959), but ever since highly sophisticated systems have been developed that apply Darwin’s ideas of natural evolution to the artificial world of computers and modeling. Of particular interest to this work are the Genetic Algorithms (GAs) and the Genetic Programming (GP) technique as they are the predecessors of Gene Expression Programming (GEP), an extremely versatile genotype/phenotype system. The way Automatically Defined Functions (ADFs) are implemented in GEP is another example of the great versatility of this algorithm and the versatility of GEP ADFs opens up new grounds for the creation of even more sophisticated artificial learning systems. So let’s start by introducing briefly these three techniques in order to appreciate the versatility of the genotype/phenotype system of Gene Expression Programming with and without ADFs.

In this chapter, we discuss the actions of antidepressants in the context of central nervous system (CNS) plasticity. The term “plasticity” implies that the CNS can adapt to conditions that threaten the physical and psychic/emotional well-being of the organism by altering programs of gene expression in specific neuronal and glial cell populations (1–3). These changes affect the construction and pruning of synaptic connections, the concentrations of enzymes and ion channel proteins, the rates of synthesis, release, and degradation of neurotransmitters and neuromodulators, and the densities and activities of their receptors. Ultimately, changes in programs of gene expression determine the intensities of incoming signals, the sensitivities of neuronal systems to those signals, and the nature, amplitude, and duration of CNS responses.

The emergence of molecular neurobiology is rapidly changing the traditional focus of antidepressant drug research with emphasis on effector sites beyond the receptors. This switch in emphasis is leading to new conceptual and methodological approaches to understanding the mode of action of antidepressants. Events beyond the receptors—intracellular signal transduction pathways and regulation of programs of gene expression—are promising new and exciting targets for antidepressants. A short historical review of the evolution of hypotheses (e.g., monoamine, b-adrenoceptor down-regulation, and the 5-HT/NE/ glucocorticoid link) concerning the mode of action of antidepressants is first presented. The chapter then presents an overview of new hypotheses concerning the mode of action of antidepressants and discusses the role of N-methyl-D-aspartate (NMDA) receptors in antidepressant action and the possible role of corticotropin releasing factor (CRF) antagonists and substance P receptor antagonists as antidepressants. Neurotransmitter-induced intracellular processes and the importance of crosstalk at the level of protein kinases are described. The chapter than discusses protein kinase C-related processes, G proteins, and transcription factors as potential targets for antidepressants and considers the convergence of neurotransmitter signals beyond the receptors at the level of protein kinase-mediated phosphorylation. To discover the next generation of antidepressants, two avenues of interrelated investigations seem promising: (1) a more rigorous elucidation of the molecular psychopathology of affective disorders and the development of animal models of depression with greater disease validity and (2) the development of new methodology to explore mechanisms beyond the receptors and second messengers in animal models of depression and in patients with affective disorders. Possible models of depression that may have increased disease validity are described. The chapter concludes with a discussion of how programs of gene expression are likely to affect antidepressant drug development. Differentially expressed genes and their protein products can be used as novel drug targets for the development of the next generation of antidepressants, which hopefully will meet the yet unmet criteria of greater efficacy, shorter onset of therapeutic action, and efficacy in therapy-resistant depression.

Developing the seismic fragility analysis with fuzzy random variables using Mouth Brooding Fish algorithm