Civil Engineering Applications Research Articles

Optimized and improved DNN predictive model of fatigue life parameters in hybrid fiber reinforced self-compacting concrete

ABSTRACT Hybrid fiber-reinforced self-compacting concrete (HyFRSCC) represents a cutting-edge material in civil engineering, combining the benefits of self-compacting concrete with enhanced mechanical properties conferred by fiber reinforcement. As a versatile solution, HyFRSCC offers improved durability, sustainability, and structural performance compared to conventional concrete formulations. The novel techniques for improving the analysis and design of HyFRSCC structures with an emphasis on improving cost-effectiveness, sustainability, and durability in civil engineering applications. A novel approach that combines enhanced deep neural network (DNN)-based advanced predictive modeling with self-improved coati optimization (SI-COA) methodologies. By precisely predicting the Weibull distribution parameters which are essential for fatigue life analysis this method seeks to provide reliable characterization of HyFRSCC behavior under various stress conditions. Hybrid fibers, which frequently combine steel with synthetic or polymer fibers, improve concrete's resistance to cracking. The requirement for frequent maintenance or repairs is decreased by HFRSCC, which increases the longevity and durability of concrete structures. Hybrid fibers, which frequently combine steel with synthetic or polymer fibers, improve concrete's resistance to cracking. The concrete's ability to withstand freeze–thaw cycles is enhanced by fiber reinforcing. Fibers are added to concrete to improve its abrasion resistance and long-term performance under mechanical wear in applications where abrasion is an issue, such as industrial floors and pavements. When 60% of the training data is used, the Improved DNN model outperforms LSTM(47.00), DenseNet (44.70), MobileNet (49.49), ResNet (48.85), CNN (45.40), Bi-GRU (46.92), and DNN (42.52) with an MAE of 38.58.

Physical, Chemical, and Mechanical Characterization of Coated Date Palm Leaf Fiber from the Middle Region of Iraq for Potential in Civil Engineering Application

Date palm leaf fibers are suitable for engineering applications due to their availability, inexpensiveness, and ecofriendliness. However, there is a risk of biodegradation in the long term. This paper explores date palm leaf fiiber (DPL) properties and the protection of the fibers from biodegradation to enhance their lifespan. To this end, two coating materials (bitumen and polyurethane) were used separately. Physical and mechanical tests were conducted to determine the most effective material to coat the DPL fibers. To comprehensively assess the performance of the coated fibers, their morphology was examined via microstructure analysis using scanning electron microscopy (SEM) and energy dispersive X-ray analyses (EDX) tests. This analysis encompasses their material properties, chemical composition, water absorption, and degradation, providing a thorough understanding of the protective coatings’ impact on the DPL fibers. The tensile strength test results revealed that the maximum tensile strength of the bitumen coated date palm fiber (DPLB) is 7.4 MPa. The tensile strength is two times greater than the polyurethane coated date palm leaf fiber (DPLP) and untreated date palm leaf fiber (UDPL). The results of the degradation test revealed that the weight loss percentage is equal to 45.5 and 25 in the case of the UDPL and DPLP fibers, and no loss in weight in the case of the DPLB fiber. Out of all the test results, bitumen is considered the best due to its ability to resist the attack of chlorides and sulfate ionspresent in groundwater on top of being cheap, simple, and efficient.

Physico-Mechanical Properties of Alkali-Activated Based Composites Using Recycled Tire Fibers

Used tires (UTs) are a global problem, especially in developing countries due to inadequate management systems. During retreading, when the worn tread is replaced, waste is generated in the form of tire fibers (TFs) and particles, which can be reused as raw materials to produce economically and environmentally low-cost prefabricated elements. Using TFs as a lightweight aggregate in nonstructural geopolymer-based elements is a sustainable valorization option. This study aims to valorize used tires by incorporating them as TFs into lightweight geopolymer mixes and analyzing their physico-mechanical, thermal, and thermography properties for building and civil engineering applications. The geopolymer is produced from a precursor (spent catalyst residue from catalytic cracking, FCC) and an alkaline activator composed of rice husk ash (RHA), sodium hydroxide, and water. The control sample’s (mortar with siliceous sand, CTRLSIL) compressive strength came close to 50 MPa, while the TF mixes ranged from 32 to 3 MPa, which meet the masonry standards. The thermal conductivity and thermography analyses showed that increasing the TF content reduced the heat transmission and achieved a similar performance to expanded-clay concrete and better performance than for conventional concrete.

Comparative analysis of deep learning models for crack detection in buildings

Life-time of the buildings is generally challenged by the act of nature. In-spite of the fact that the constructions provide minimum guarantee on quality and durability, certain mismatch in the composition of the materials, stress on the building, and chemical or physical imbalance of the materials, lead to surface crack. Cracks are also generated due to the shuffle of climatic conditions, which leads to the contraction and expansion of the building surfaces, and other damages. The guarantee on building safety and serviceability depends on how these buildings are successfully assessed and maintained. The development of Artificial Intelligence (AI) techniques, provide favourable solutions in-order to handle, manage and solve building cracks, through analysis using deep image neural network models, that perform classification of the building with crack images. As a result, a critical challenge for many civil engineering applications is the precise, quick, and automated identification of cracks on structural surfaces is addressed with the solutions provided by the deep image neural networks. In this research, we tackle the research gap and data scarcity by developing and curating a novel deep learning image processing for detecting cracks in brickwork. We also train and validate several deep learning models to classify brickwork images as either cracked or normal. The dataset of the proposed work contains 24,000 images which are classified through binary classes. These classes are generated for crack and non-crack images. The various parameters such as Batch size, Pooling, Activation functions Learning-rate, Kernel-Size, Normalization, and Optimizers are used for the evaluation of the model. The proposed work performs a comparative analysis of four deep image models such as Inception V3, VGG-16, RESNET-50 VGG-19, Inception ResNetV2 and CNN-RES MLP. With the analysis of all these models, the Inception V3 provides the best of all with the accuracy value of 99.98%. The InceptionV3 tops the Precision value of 99.99% and RESNET-50 tops the Recall value of 99.98%. The IncpetionV2 provided the best of the Region of Convergence value of 0.9999 which is the best among all the models for reliable and stable performance.

Laboratory Tests Using Distributed Fiber Optical Sensors for Strain Monitoring.

Using fiber optics as a tool for different kinds of geotechnical monitoring can be highly attractive and cost-effective when compared to conventional instruments, such as piezometers and inclinometers, among others. A single fiber optic cable may cover a larger monitoring area compared to conventional instrumentation and allows for monitoring more than one physical quantity with the same fiber optic cable. The literature provides several different examples of distributed fiber optic systems usage. For using any sensor, a calibration curve and parameters are required. In the case of strain sensors, calibration is required to derive strain values from the frequency measurement quantity. However, fiber optic sensor cable manufacturers do not often provide cable calibration parameters, and researchers should consult the specialized literature. This article thus presents a bench adjusted for tests with single-mode fiber optic cables, as well as results of tensile tests for defining the function of strain variations in two different optical fiber cables manufactured by different companies using two different distributed interrogators. This paper also proposes a methodology for calibrating fiber optic cable deformation. A few manufacturers of fiber optic cables aim at civil engineering applications. Therefore, we propose a calibration methodology to show the possibility of obtaining calibration parameters of any fiber optic cable, even those manufactured for telecommunications purposes and not only for cables manufactured for civil engineering use. Thus, researchers will not be restricted to the acquisition of special cables for their applications. The results allowed us to conclude that the application of calibrated fiber optic sensors to experimental pile foundations permits the evaluation of the load-displacement behavior of these elements under different loading conditions.

Experimental models for cohesive granular materials: a review

Granular materials are involved in most industrial and environmental processes, as well as many civil engineering applications. Although significant advances have been made in understanding the statics and dynamics of...

Modeling of Functionally Graded Material Shells

In this research, we propose an algorithm to study the buckling of thin Functionally Graded Material (FGM) shells, utilizing a novel implementation of the asymptotic numerical method (ANM). Our approach integrates a three-step process: representation of variables and loading conditions through a truncated Taylor series, discretization using the finite element method (FEM), and advancement via a continuation method. This method is particularly effective for tracking the solution curve through systematic matrix inversion and tolerance adjustments. By applying this robust algorithm within the framework of Kirchhoff-Love theory, we analyze how the properties of FGM shells vary smoothly from metal at the base to ceramic at the surface, crucial for applications in aeronautics and civil engineering where stability under load is paramount. The results are validated against the Abaqus industrial code, demonstrating the accuracy of our model. Furthermore, we detail the impact of the volume fraction index on the load-displacement behavior and structural deformations, providing valuable insights for enhancing the design and safety of these critical structures.

Impact of cylinder corner modification on fluid dynamics and heat transfer: Corner cut size and cut angle

The impact of corner modifications on fluid dynamics and heat transfer characteristics of a square cylinder is numerically investigated at a Reynolds number Re = 150 (based on cylinder width W), with corner cut size C* (= C/W) = 0–0.5 and cut angle α = 0°–45°, where C is the corner cut size. The corner cut modification gradually modifies the baseline square cylinder (C* = 0, α = 0°) to a number of octagonal and hexagonal shapes and finally to a diamond cylinder (C* = 0.5, α = 45°) through a total of 64 distinct shapes. The focus is given on the dependence on C* and α of time-averaged and fluctuating flow fields around the cylinders, Strouhal numbers, fluid forces, and time-mean Nusselt numbers. Results of fluid forces and Nusselt number are also compared to those of circular cylinders at the same Reynolds number. Time-average and fluctuating fluid forces are reduced for several modified shapes compared to the baseline case, with some modifications yielding forces even smaller than those observed for the circular cylinder. Heat transfer improves for all modified shapes relative to the baseline square cylinder yet remains lower than that of the circular cylinder. The time-averaged flow fields reveal three distinct wake patterns depending on C* and α. The study identifies several shapes with improved fluid dynamic characteristics that can be applied in mechanical, civil, and environmental engineering applications.

Prediction of normalized shear modulus and damping ratio for granular soils over a wide strain range using deep neural network modelling

ABSTRACT Dynamic properties, such as shear modulus and damping ratio, are critical for civil engineering applications and essential for accurate dynamic response analysis. This study introduces a novel Deep Neural Network (DNN) approach to predict the normalized shear modulus (G/Gmax ) and damping ratio (D) of granular soils over a wide strain range. Utilising a comprehensive dataset from cyclic triaxial (CT) and resonant column (RC) tests, we developed a Deep Feed-Forward Neural Network (DFFNN) model. The model incorporates grading characteristics, shear strain, void ratio, mean effective confining pressure, consolidation stress ratio, and specimen preparation method as inputs. The DFFNN demonstrated high accuracy with testing results of 0.9830 for G/Gmax and 0.9396 for D, outperforming traditional empirical models and other intelligent techniques such as Shallow Neural Network (SNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). This data-driven approach offers a robust and adaptable method for predicting the dynamic properties of granular soils across diverse conditions.

Evaluating the Efficiency of Dust Stone as an Improvement Material for Cement Gravel Column

This research explores the feasibility of incorporating dust stone into cement gravel columns to enhance their mechanical properties and sustainability profile. A series of laboratory experiments including unconfined compressive strength (UCS) and permeability tests were carried out. 15 mixture designs were prepared to investigate the effect of stone dust (5-20% of cement and 0, 5, 10, 15, and 20% by weight of highest porous sample) on volume fractions of CG samples on engineering and physical properties of cemented gravel samples, at 7 and 28 days. The findings indicate that the ideal proportion of stone dust is 10%, which significantly enhances the compressibility of the CG mixture while preserving satisfactory permeability. The finding indicates that stone dust is a feasible and efficient additive for improving the performance of cement gravel columns in civil engineering applications. Moreover, stone dust provides sustainability advantages by minimizing waste production and contributing to a circular economy.

Life Cycle Assessment of End-of-Life Tire Disposal Methods and Potential Integration of Recycled Crumb Rubber in Cement Composites

Globally, 1.5 billion annual tire outputs generate a substantial volume of end-of-life tires (ELTs), creating significant environmental challenges. Despite increased recovery rates, ELT management costs in Europe underscore the need for proactive strategies to mitigate environmental and health risks. This study comprehensively evaluates the environmental impact of disposal methods, including landfilling, incineration, and crumb rubber production, using Life Cycle Assessment (LCA) via the OpenLCA software 2.0.2. While incineration is sometimes identified as a disposal method, unprocessed scrap tires have potential applications in civil engineering that can better align with sustainability goals. Detailed ELT composition analysis reveals significant recycling potential, with car and truck tires containing 10–20% steel fiber content, less than 1–8% textile fibers, and approximately 80% natural and synthetic rubber content. Recycling 1 ton of ELTs saves an estimated 1.4–1.6 tons of CO2 Eq. compared to incineration. Mechanical recycling and application of recycled crumb rubber in concrete show significant environmental advantages, reducing mass density by approximately 55% and enhancing ductility by up to 40%, according to material testing results. These properties make crumb rubber particularly suitable for acoustic and resilient applications. Additionally, its elasticity and durability offer effective solutions for shoreline reinforcement, mitigating erosion and providing stability during flooding events. When used as a replacement for river sand in cement composites, crumb rubber contributes to a 24.06% reduction in CO2 emissions, highlighting its potential for environmentally friendly construction.

Optimum design of reinforced concrete beam sections with JAYA algorithm

Section design is an important process for designing reinforced concrete structures because of the existence of factors such as bearing capacity and cost. After defining the initial cross sections for reinforced concrete elements, reinforcement amounts are calculated within the framework of certain rules and regulations. In the classical method, optimum cost and reliable structure can be designed by trial and error. Designing with the classical method is quite time-consuming. As in different fields, metaheuristic algorithms are employed to civil engineering applications to reach the optimum solution. In this study, unlike other examples from the literature, the optimum cost design of reinforced concrete beam cross-section was done, adding torsional moment to bending moment and effects of shearing force through the use of the JAYA algorithm. The optimum cost design of a reinforced concrete beam section under the effects of torsional moment, bending moment and shear force is performed. The design is done according to the rules specified in ACI 318 (Building code requirements for structural concrete), as it is well-known and used in many international projects. For the purpose of this study, cross-sections of 10 different reinforced concrete beam cross-sections were designed under 5 different loads for 2 different concrete classes through MATLAB software, with the aim of finding the optimum beam cross-section. A reinforced concrete beam is designed under different torsional loads and for different concrete classes and it is aimed to find the optimum beam cross-section. It shows the effect of torsional force on reinforced concrete beam cross section and reinforcement by using different torsional loads. It was observed that the algorithm used tends to approach the optimum result, increasing the area of concrete with lower unit cost or reducing the distance between stirrups. It was concluded that Jaya algorithm performs effectively with respect to optimizing reinforced concrete beams. The algorithm used can be applied to different reinforced concrete elements.

Experimentation Evaluation of Dynamics of Steel Wire Rope

Wire ropes represent a distinct category of ropes synthesized through the intertwining or braiding of individual steel wires. This unique construction confers notable attributes such as strength, flexibility, and durability to the resultant rope. The pervasive wire ropes across diverse industries underscores their capacity to adeptly manage substantial loads and endure adverse environmental conditions finding its application in mechanical, civil, mining, and marine engineering. This paper presents usage of image processing method to detect the deflection of a steel wire rope. The system comprises of dividing the wire rope into different sections, spatial referencing, frame separation, color-based detection, morphological operations, data collection and visualization. The steel wire rope deflection program will allow designers to conveniently process the transverse deflection trajectories of a steel wire rope in real time. One may further introduce any control actions when the deflection distance reaches a threshold value. The image-based algorithm enabled a robust detection of the deformed shape as a function of time, thus obtaining its dynamic trajectories. The deflection shapes and trajectories are compared with numerical predictions made using Cosserat Rod theory, which considers the geometric nonlinearities introduced due to large deflection. The numerical solution gave a rough estimate of the static deformation state of the wire, which agrees with the experimental results. This study will be useful for Structural Health Monitoring, Safety Assurance, Fatigue Analysis and Performance optimization. Moreover, the Continuum Mechanics employs Cosserat rod theory to model continuum robots which will provide enhanced computational efficiency and better dynamic simulation capabilities[18]. The deflection shapes and trajectories are compared with analytical predictions made using Cosserat Rod theory, which considers the geometric nonlinearities introduced due to large deflection.

Boundary element method for buckling analysis of elastically connected double-beam system

Studies of Double-beam systems have received significant attention from researchers due to their wide applications in civil and mechanical engineering. Commonly, finite element method or exact solutions have been employed to solve double-beam systems, with few others considering buckling of axially loaded double-beam systems with classical boundary conditions. This paper presents a novel formulation of the Boundary Element Method (BEM) to determine the critical buckling load of the double-beam system elastically connected by a Winkler elastic layer with generalized boundary conditions. Based on the Euler-Bernoulli beam theory, the double-beam system is composed of two identical beams. This paper provides a detailed discussion of each step involved in the BEM, including the fundamental solution, boundary integral equations, and algebraic system. Examples considering different boundary conditions, material properties, and load cases are done and compared to corresponding analytical solution. The results show excellent agreement between the BEM approach and the analytical solution, confirming the accuracy and effectiveness of the technique.

Evaluation of self-healing in reactive powder concrete with urea–formaldehyde/epoxy microcapsules

The use of microcapsules as preservative vessels for healing materials has led to a great revolution in the repair of materials. Microcapsules have been used in medicine, agriculture, metallurgy and mechanics. In civil engineering applications, microcapsules are usually used for the self-healing of concrete, asphalt and cementitious materials. Concrete and cement are widely used in civil engineering and are the predominant construction materials worldwide. The objective of this study was to design and produce urea–formaldehyde microcapsules for the recovery of reactive powder concrete (RPC). It was found that RPC specimens with and without microcapsules exhibited different behaviours. All of the RPC specimens containing microcapsules were found to have lower strengths than specimens without microcapsules. The smallest reductions in compressive strength were observed in specimens with a microcapsule content of 4–6% by weight of cement. The healing ratio of compressive strength increased with an increase in the weight percentage of microcapsules. Scanning electron microscopy and energy-dispersive spectroscopy were used to observe the process of crack healing, and the results showed that the cracks were filled with healing products.

Thermoelectric properties of multifunctional metashale mortar with carbon fiber dust

Abstract The ability of thermoelectric geopolymer mortars to convert waste heat into electrical energy significantly expands their potential in civil engineering applications. The effectivity of this conversion, expressed by the figure of merit (ZT), is positively influenced by three material parameters, high electrical conductivity, the Seebeck coefficient, and low thermal conductivity. Doping a typically nonconductive geopolymer matrix with electrically conductive admixtures (ECAs) positively influences electrical conductivity, which is an important presumption to gain energy harvesting function. The paper deals with the design and material characterization of geopolymer metashale mortar doped with 5 wt.% of carbon fiber dust (CFD), with special emphasis on the determination of thermoelectric properties. On the basis of the observed thermoelectric voltage, the Seebeck coefficient and the ZT were calculated and discussed.

Tye Programming and Designing Surveying and Civil Engineering Application for Android

Abstract In response to the technical revolution that the world is experiencing, the mobile phone represents one of its most prominent features. Engineers face some problems at work sites that require software to perform calculations such as calculating coordinates and areas. This research aims to present the design of an engineering application for mobile phones for surveying and civil engineers to facilitate the calculations that they need at work sites to save time and effort. This paper was started using Matlab and then Android Studio to design the application interface and to make the connection between the elements of the user interface, where some surveying calculations were programmed that may take some time to calculate manually and also take advantage of the GPS that the device contains to determine the location and do some surveys such as calculating the distances, areas and also raising points for a specific area, the application was raised on the Google Play platform. The application can be downloaded to the smartphone by searching Google Play with the name Surveying Calculations or through the link. https://play.google.com/store/apps/details?id=com.uob.zevlog

An Article Review on Vision-Based Defect Detection Technologies for Reinforced Concrete Bridges

Bridges are crucial and the most vulnerable element in the infrastructure systems. A major challenge is to maintain bridge structures at a sufficient level of safety. Scheduled inspections in these structures are important to prevent any failure. The requirement of periodic inspection is urgently needed to maintain the bridges in safe operating condition for the public. Visual inspection is currently the main form for the flaw’s inspection. Nevertheless, it is suffering from time consuming and some limitations related to subjectivity and uncertainty. Due to the complexity of bridge structure, automatic defect detection is an urgent requirement for reinforced concrete bridges. In view of this, the creation and utilization of computer vision method has received considerable attention in several applications of civil engineering. Thus, this paper introduces a comprehensive study in computer vision-based defect detection related to concrete bridges. In this study, a detailed survey is undertaken to identify the research problems and the accomplishments to date in this field. Accordingly, 50 studies between peer-reviewed publications and conference papers Scopus found in are reviewed. Through the analysis, the current review divided the image technology into three groups based on: 1) image processing; 2) machine learning; and 3) quantifying the severity of defects by identifying their parameters. This article highlights the difference and the advantages and disadvantages of applying image processing techniques and machine learning. The paper identifies the types of defects detected by image technology in previous studies and their shortcomings in determining some parameters related to those defects. Finally, this research addresses issues related to the efficiency of detection and the main factors to be considered that may help further research in image-based approaches for defect detection effectively in concrete bridges.

Effect of Various Notch Shapes on Lamb Wave Scattering Behavior in a Bent Plate

Abstract The application of guided waves to investigate commonly used plate shapes in the aerospace, mechanical, and civil industries is plates with bend shapes. This article investigates the interaction of fundamental Lamb waves with notches in bent plates, commonly found in aerospace, mechanical, and civil engineering applications. These areas are particularly susceptible to failure due to defects such as cracks and notches, which often manifest as semicircular corrosion patches or 90-deg notches. The presence of notches affects stress distribution, necessitating thorough analysis to prevent accidents. Accordingly, this article focuses on the interaction of fundamental Lamb waves through two types of notches that could be present inside a bent metal plate section. To explore this, a hybrid numerical framework is employed which combines semianalytical finite elements (SAFEs) with the finite element method (FEM). A bent plate section with various notch types is simulated using FEM, while SAFEs facilitate the definition of wave propagation through healthy regions of the plate. The study analyzes the scattering behavior of Lamb waves for different notch configurations and examines both fundamental modes over a specified frequency range. With a change in the interrogation signal parameters, there is a noticeable difference in the sensitivity of scattered waves with different notch types. Formulating a strategy for identifying and locating a notch inside a bent plate may need careful consideration of the important conclusions drawn. Understanding these interactions, the aim of the article is to enhance the integrity assessment of structural components subject to such defects.

Transfer learning in bridge monitoring: Laboratory study on domain adaptation for population-based SHM of multispan continuous girder bridges

The presence of sufficient labelled data associated to various environmental conditions and damage scenarios often represents a challenge for the applicability of supervised-learning methods when dealing with structural health monitoring of real-scale infrastructures. To address this problem, population-based structural health monitoring has been recently proposed as an attractive solution, with the goal to collect information from similar structures and transfer health-state labels across the population. This paper focusses on the use of a feature-based transfer learning method. A machine-learning model is trained with source labelled data in a transformed features space to afterwards classify the unlabelled target dataset of a different bridge. More specifically, a domain adaptation-based methodology proposing two possible strategies, a single-source or a multi-source approach, is described. Given the difficulties in validating these techniques on real and varied datasets from multiple bridges, this paper presents a physical benchmark for population-based structural health monitoring in civil-engineering applications. The transfer between different configurations of a laboratory-scale bridge model, subjected to multiple experimental tests under changing environmental conditions and to the same pseudo-damage scenarios, is investigated. The results of the experimental campaign demonstrate the possibility of effectively exchanging damage labels to perform novelty detection and damage classification across the population via domain adaptation, improve the identification of specific damage classes, as well as to increase model’s outcome using a multi-source approach, thus overcoming the limitations of conventional machine learning-based methods. Furthermore, this paper provides an open dataset with the physical benchmark-related data, allowing other researchers to test their own algorithms and address the various transfer learning challenges.