Civil Engineering Problems Research Articles

Modelling the Shear Flow Behaviour of Cement Paste Using Machine Learning –XGBoost

In recent years machine learning is considered as a highly effective and widely used tool to predict the behaviour of complex and heterogeneous problems. In this paper, the behaviour of superplasticised cement paste is assessed by XGBoost, which is accepted to accomplish the state-of-the-art results on many machine learning challenges. The data required for the development of model is formulated experimentally by conducting rheological tests on cement pastes using a temperature controlled Coaxial Cylinder Viscometer. Various parameters like amount of cement, superplasticiser, water and test temperature are taken as input parameters and the behaviour is assessed by taking rheological characterises like yield stress and plastic viscosity as output parameters. Out of the 252 data formulated experimentally 85 % are used for training and the remaining is for testing the efficacy of the network. From the results it is observed that the model developed using XGBoost is a promising tool for the solution of highly complex and heterogeneous civil engineering problems, which otherwise is highly tedious and time consuming in nature.

Prediction accuracy of underground blast variables: decision tree and artificial neural network

Accurate prediction of blast induced ground vibration variables such as particle velocity and frequency are of interest for safe design of controlled blasting operations for mining, tunnelling or excavation projects. There are certain limitations in the widely used empirical and numerical approaches especially when number of variables is large. Various data driven approaches have been employed for producing correct estimates for such cases. Decision tree (DT), earlier successfully employed for solving variety of civil engineering problems, is employed for prediction of blast variables for the first time in this article. The performance of DT models was found to be equally good (for particle velocity variable) or better than (for frequency variables) ANN models developed in this study, and unequivocally superior to the SVM or RF models reported in literature. Additionally, the clarity in decision rule-based estimation foster easy comprehension and future implementation.

Investigation of Phase Portraits Belonging to Polynomial Dynamic Systems in a Poincare Disk

The paper describes especially developed research methods and results for some original study of a broad dynamic systems’ family, which is characterized with the reciprocal polynomials in the right parts of differential equations which compose a system. The goal of this work is to obtain and analyze all the existing (and different in the topological meaning) phase portraits in a Poincare disk, as well as to indicate the criteria of each topological type of phase portraits appearance. Poincare methods of sequential transformations and displays have been successfully used along with a whole set of new research techniques developed for the purposes of this study. Over than 250 of different portraits were obtained and depicted eventually for different subsystems belong to the dynamic systems’ family under consideration. All portraits were described up to each the so-called invariant cell of the phase portrait, a boundary of it, and features of a phase flow in it, including it’s a source and a sink. Results of dynamic systems’ behavior research are useful for a wide spectrum of applications such as the mathematical modeling of physical processes, vital problems of civil engineering, for example, in the consideration of seismic stability of buildings etc., computing and producing systems, biological phenomena, and of sociological events.

Soil structure interaction in framed structure on a hilly area: a case study

There are many problems in civil engineering which are involving some kind of structural element in straight connection with the land. When there is some forces are applied to the structural element, both component (structural element and soil/ground) must deform and move in compatible manner. These types of problems are referred as Soil-Structure Interaction (SSI) problem. In this project, an analysis of a building on the slope (10˚) with 5 stories was carried out.The soil under the building are make up by 8 layers consist different type of material. The study in this project is focusing on development of finite element model in order to analysis of a framed structure foundation comprised of a pile system and soil media resting on a hilly area. A beam bending element that calculate the outcome of transverse shear deformation and the axial interaction is then formulated. Based on the interaction behavior of the superstructure foundation soil system, bending moment in the superstructure is found to increase in the external beam and decrease in the inner beams.The effect of hill slope influences the displacements, and subsequently, the forces in the superstructure. This study also compares the result from SSI program and conventional analysis to find out the weakness of the conventional analysis.

Tracking the 6-DOF Flight Trajectory of Windborne Debris Using Stereophotogrammetry

: Numerous post-windstorm investigations have reported that windborne debris can cause costly damage to the envelope of buildings in urban areas under strong winds (e.g., during hurricanes or tornados). Thus, understanding the physics of debris flight is of critical importance. Previously developed numerical models describing debris flight physics have not been validated for the complex urban flow environment; such a validation requires experimentally measuring the debris flight trajectory in wind tunnel tests. In this context, this paper proposes a debris measurement algorithm using stereophotogrammetry. This algorithm aims to determine the six-degree-of-freedom (6-DOF) trajectory and velocity of flying debris, addressing the research gap, i.e., the lack of an algorithm/software for measuring three-rotational-DOF using stereophotogrammetry. This is a civil engineering problem, but computer graphics is the foundation to solve it. This paper focuses on the theoretical development of the algorithm. The developed algorithm can be readily implemented in modern wind tunnel experiments.

Comparative Analysis of Static and Dynamic Mechanical Behavior for Dry and Saturated Cement Mortar.

Deformational and breakage behaviors of concrete and cement mortar greatly influence various engineering structures, such as dams, river bridges, ports, tunnels, and offshore rig platforms. The mechanical and petrophysical properties are very sensitive to water content and are controlled by the liquid part in pore spaces to a large extent. The objective of this paper is to investigate the water saturation effect on the strength characteristics and deformability of cement mortar under two loading conditions, static and dynamic compression. A set of cement mortar samples was prepared and tested to study the mechanical behavior in dry and saturated states. The first part of the research incorporates the study of static mechanical properties for dry and brine-saturated cement mortar through uniaxial compressive strength tests (UCS). Second, drop-weight impact experiments were carried out to study the dynamic mechanical properties (impact resistance, deformation pattern, and fracture geometry) for dry and saturated cases. The comparative analysis revealed that water saturation caused substantial changes in compressive strength and other mechanical characteristics. Under static loading, water saturation caused a reduction in strength of 36%, and cement mortar tended to behave in a more ductile manner as compared to dry samples. On the contrary, under dynamic loading conditions, water saturation resulted in higher impact resistance and fracture toughness as compared to dry conditions. In addition, fractures could propagate to smaller depths as compared to dry case. The study will help resolve many civil, mining, and petroleum engineering problems where cement structures undergo static as well as dynamic compression, especially in a hydraulic environment where these structures interact with the water frequently. To the best of our knowledge, the effect of water saturation on the dynamic mechanical properties of cement mortar has not been well understood and reported in the literature.

Tackling problems in civil engineering caused by the presence of pyrite

Although the oxidation of pyrite in geological materials has been linked to various problems in civil engineering works for many years, the risk of further problems has not been eliminated. Pyrite occurs widely as a minor constituent of geo-materials and many construction processes result in the exposure of pyrite-bearing materials to air and water, which then result in oxidation. The paper presents an historical review of construction scenarios in which pyrite has had significant adverse impact. The cases considered include rapid deterioration of rock materials, chemical attack on concrete and metallic structures in the ground, and the expansion of fills and stabilized soils. These cases have been important turning points in understanding deleterious pyrite processes and the development of guidance for their avoidance. By following the guidance presented in the relevant standards and technical documentation, destructive processes due to pyrite should be avoided. However, as many of the problems are influenced by variables, such as site location factors and construction processes, adhering to published guidance may not eliminate the possibility of adverse impacts. It is important that expert technical advice is provided at all stages of projects and that any deviations from the expected performance should be properly evaluated.

Environmental Benefits of Cement and Areca Nut Husk Ash on Behavior of Clay and Red Clay

The swelling and shrinkage characteristics of clay soil types cause many problems in civil engineering works like Laying, buildings and roads constructions. These soil types tend to put challenges to civil engineers as their shearing strength and bearing capacity are extremely low. Many studies have been made to rectify these challenges by using different materials and various innovative ways that could possibly improve the soil engineering properties, as well as reduce environmental hazards in a cost-effective way. This study focuses on the use of areca nut husk ash and cement as admixture to improve the stabilization properties of clay and red clay soil types. The stabilization of soil was tested by adopting the standard methods such as Atterberg’s limit test, Standard proctor test and unconfined compressive strength test by mixing equal proportion of admixture to both soil types with varying percentages of clay (0%, 3%, 4%, 6% and 8%) and red clay (0%, 2%, 3% and 4%) soils. From the results, it was observed that clay soil with 6% of admixture and red clay with 3% admixture were optimum and the findings clearly showed that the stability of clay and red clay soil, can be improved by admixture containing areca nut husk ash and cement and this can be used as a good ground improvement technique in engineering projects.

Modeling Construction labour productivity using Evolutionary Polynomial Regression

Construction projects are labour-intensive and labour costs are a substantial percentage of total budget. Impaired labour productivity causes an increase in construction project schedule. Labour productivity is one of the most frequently discussed topics in the construction industry, and modelling labour productivity by utilising different techniques has been getting more attention. It is a challenging task as it requires identifying the influencing factors as well as considering the associated interdependencies. This paper investigates the application of evolutionary polynomial regression (EPR) for modelling labour productivity in formwork installation. EPR is a data-driven hybrid modelling technique based on evolutionary computing and has been successfully applied to solving civil engineering problems. Results obtained from the EPR model were compared with the outcomes of three other methods: best subset, stepwise, and general regression neural network (GRNN). Results demonstrate the predictive superiority of the developed EPR model for nonlinear problems based on statistical performance indicators.

A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices.

Due to the nature of real-world problems in civil engineering, students have had limited hands-on experiences in structural dynamics classes. To address this challenge, this paper aims to bring real-world problems in structural dynamics into classrooms through a new interactive learning tool that promotes physical interaction among students and enhances their engagement in classrooms. The main contribution is to develop and test a new interactive computing system that simulates structural dynamics by integrating a dynamic model of a structure with multimodal sensory data obtained from mobile devices. This framework involves integrating multiple physical components, estimating students’ motions, applying these motions as inputs to a structural model for structural dynamics, and providing students with an interactive response to observe how a given structure behaves. The mobile devices will capture dynamic movements of the students in real-time and take them as inputs to the dynamic model of the structure, which will virtually simulate structural dynamics affected by moving players. Each component of synchronizing the dynamic analysis with motion sensing is tested through case studies. The experimental results promise the potential to enable complex theoretical knowledge in structural dynamics to be more approachable, leading to more in-depth learning and memorable educational experiences in classrooms.

Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation

AbstractEmploying Deep Learning (DL) technologies to solve Civil Engineering problems is an emerging topic in recent years. However, due to the lack of labeled data, it is difficult to obtain accurate results with DL. One commonly used method to tackle this issue is to use affine transformation to augment the data set, but it can only generate new images that are highly correlated with the original ones. Moreover, unlike normal natural objects, distribution of structural images is much more complex and mixed. To address these challenges, Generative Adversarial Network (GAN) can be one feasible choice. We introduce one specific generative model, namely, Deep Convolutional Generative Adversarial Network (DCGAN) and propose a Leaf‐Bootstrapping (LB) method to improve the performance of this DCGAN. To effectively and quantitatively evaluate the quality of the synthetic images generated by DCGAN to complement human evaluation, Self‐Inception Score (SIS) and Generalization Ability (GA) are proposed. We also propose a pipeline based on Transfer Learning (TL) using synthetic images to help enhance a weak classifier performance under the condition of low‐data regime and limited computational resources. Finally, we conduct computer experiments with the proposed methods for two scenarios (scene level identification and damage state check) and one special synthetic data aggregation case. The results demonstrate the effectiveness and robustness of the proposed methods.

Locating structural changes in a multiple scattering domain with an irregular shape.

Locadiff is a method for imaging local structural changes in a random, heterogeneous medium. It relies on the combination of a forward model to calculate the sensitivity kernel of the source-receiver pairs, with an inversion method to determine the position of the changes. So far, the sensitivity kernel has been evaluated based on an analytical solution of the diffusion equation, which lacks the flexibility to handle problems where the domain has boundaries with an irregular shape. Moreover, the accuracy of the previous inversion method, based on linear algebra tools, was very sensitive to the values of the inversion parameters. This paper introduces a more generic approach to solve both these issues. The first problem is tackled by the implementation of a numerical method as an alternative for solving the diffusion equation. The second problem is tackled by the introduction of enhanced optimization algorithms to improve the stability of the inversion. This improved version of Locadiff is validated via both numerical examples and experimental data from an actual civil engineering problem.

Discrete Lagrangian algorithm for finding geodesics on triangular meshes

• The fast approximation method for finding open geodesics on polygonal surfaces. • A demonstration of the performance of the method and comparison with several existing methods. • a straightforward implementation. • an application in a real-world civil engineering problem. The present paper introduces an approximation method for finding open geodesics on triangular surfaces. The algorithm is specifically designed to be able to solve real world problems where geodesic paths are needed. We use the model of geodesic curvature flow for open curves in the Lagrangian formulation . The model is enriched with a tangential term in order to have a control over the quality of the discretization grid during the computation. The governing equation of the flow is solved by a numerical method based on a semi-implicit time discretization and a finite difference space discretization. The paper presents the numerical scheme and various implementation details as well as numerous experiments to demonstrate the performance of the method and to provide comparison with several other well known methods. We also present a Grasshopper component for Rhinoceros for finding optimal paths on surface meshes that we developed and that includes our algorithm.

Dimensionality reduction in thermal tomography

Estimation of material parameters plays an important role in many scientific fields ranging from geophysics, medical imaging, archaeology, material science to the preservation of historical structures. This paper focuses on the civil engineering problem of heat transfer in cases where an intervention into a structure might not be allowed and where estimation of the material parameter can be conducted using only boundary measurements. For two decades, thermal tomography has addressed such scenarios. This study introduces a novel approach for recovering spatially distributed thermal properties based on the random field theory, which efficiently parametrizes the unknown parameter fields. The proposed approach is verified computationally and the results achieved correspond well to those provided by standard thermal tomography procedures.

Selection of Impactful Environmental Parameters Using an Integrated Community Feedback System for Environmental Assessment

Environmental assessment (EA) of metropolitan areas is an appropriate method for evaluating civil and environmental engineering problems. Assessing and identifying the environmental pollution in metropolitan areas is of crucial interest, as these are used in decision making for minimizing environmental pollution. The objective of this study is to select most impactful environmental parameters using an integrated feedback to improve decision-making process and management in metropolitan areas for an environmental assessment process. The study started with 71 environmental parameters that were selected from literature study on the basis of the environmental effects on the metropolis. Subsequently, questionnaires were prepared to select the most important (controlling) environmental parameters that affect the metropolis. These questionnaires were distributed to 300 respondents of three groups. Feedback obtained was from 110 respondents (80 citizens, 11 managers, and 19 experts) in metropolitan Tehran, Iran. Statistical Package for Social Science (SPSS) software was then used to analyze the collected data. Twenty-three environmental parameters were selected based on their effects towards the metropolitan areas as a result of the SPSS analysis. These parameters comprised of 3 physical and biological (PB) parameters, 7 operational and technical (OT) parameters, 9 social and cultural (SC) parameters, and 4 economic and political (EP) parameters. Â

The influence of design and contractor errors on the failure of a tenement building

Abstract This paper presents an application of forensic engineering methods in civil engineering problems. Under the case study based on the failure of a tenement building from the 1930s, the authors want to present the analyses of the influence of design and contractor errors on this situation. These analyses were carried out using an advanced computer program - ABAQUS v.6.6. The work was split into three parts. In the first one, only the most cracked wall was analysed as a 2D model. Next, a 3D model of a building fragment with the floor removed was created. In the last stage, the entire building was modelled, including all the structural walls as well as window and door openings. In order to evaluate the percentage responsibility for the building failure, simple FA (failure analysis) was applied. The results show that the most significant influence on the current state resulted from subsequent modernization works and not from the piling works at the site to the north. These simply triggered the accumulated damage in the structure.

Developing Shuffled Frog‐Leaping Algorithm (SFLA) Method to Solve Power Load‐Constrained TCRTO Problems in Civil Engineering

It is extensively acknowledged that excessive on‐site electricity power load often causes power failure across a construction site and surrounding residential zones and can result in unforeseen schedule delay, construction quality problems, life inconvenience, and even property loss. However, energy management, such as power load optimization, has long been ignored in construction scheduling. This study aims to develop a modified shuffled frog‐leaping algorithm (SFLA) approach in project scheduling to aid decision‐makers in identifying the best Pareto solution for time‐cost‐resource trade‐off (TCRTO) problems under the constraint of precedence, resource availability, and on‐site peak electricity power load. A mathematical model including three objective functions and five constraints was established followed by the application of the modified SLFA on real‐case multiobjective optimization problems in construction scheduling. The performance of SLFA was compared with that of the nondominated sorting genetic algorithm (NSGA II). The results showed that the developed new approach was superior in identifying optimal project planning solutions, which could essentially assist on‐site power load‐oriented schedule decision‐making for construction teams.

Integration of Peridynamic Theory and OpenSees for Solving Problems in Civil Engineering

Integration of Peridynamic Theory and OpenSees for Solving Problems in Civil Engineering

Application of MOO Techniques for the Time-Cost Trade off Problems in Civil Engineering

Application of MOO Techniques for the Time-Cost Trade off Problems in Civil Engineering

Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization

With the development of science and technology, the accuracy requirements for solving engineering problems are getting stricter than before. Most structural design optimization problems in civil and mechanical engineering have proven to be the non-deterministic polynomial hard problems. The artificial bee colony (ABC) algorithm has been proven to be an effective method of design optimization problems. This paper proposes an improved ABC algorithm (DSM-ABC) combined with dual-search mechanism containing Levy flight and differential self-perturbation and applies it to three classical structural design problems, including cantilever beam design, gear train design, and three-bar truss design. The experimental results of benchmark functions from CEC2005 reveal that the proposed DSM-ABC algorithm accelerates the convergence and improves the performance. Eventually, the obtained results of optimization structural design problems prove that the DSM-ABC algorithm has a strong superiority compared with the state-of-the-art algorithms in solving optimization engineering design problems.