Field Of Civil Engineering Research Articles

Prediction and optimization of civil engineering material properties based on artificial intelligence

In the modern research field of predicting and optimizing the performance of civil engineering materials, the incorporation of artificial intelligence (AI) has provided a new perspective. The research is based on a thorough examination of the central role of AI technology and the application of different mathematical models in the task of material performance prediction and optimization, summarizing the current state of AI technology application in the field of civil engineering material performance prediction. Against this backdrop, the research further proposes a brand-new AI-based civil engineering material performance prediction method. This study, aiming to realize the optimization of material performance prediction tasks, utilized cutting-edge machine learning techniques and mathematical models, proposing this new AI prediction framework. Detailed discussions on design, implementation, and evaluation were conducted in the forecast framework, which includes a large number of tables, mathematical functions, and references. Through rigorous study and implementation, the prediction framework exhibited admirable performance, demonstrating extensive applicability for civil engineering material performance prediction and optimization. In conclusion, this new AI-based civil engineering material performance prediction method has demonstrated encouraging results. This study provides a new horizon, illustrating the tremendous potential and value of AI for predicting the performance of civil engineering materials, thereby improving efficiency and effectiveness in the civil engineering field. Overall, this new AI prediction method provides a fresh pathway for future research, making an essential contribution to anticipating and optimizing in civil engineering field.

Optimization of date palm waste fiber content and length in sand concrete using the factorial design approach

The valorization of some agri-food industry by-products, particularly plant waste, in a number of countries may be significantly interesting in the field of civil engineering because they can be used to prepare low-cost biomaterials that consume low amounts of energy and are environmentally friendly. Recently, it has been revealed that the valorization of date palm fibers (DPFs) waste and incorporating them into concrete may be one of the promising solutions that can be adopted in order to reduce or eliminate the huge amounts of this type of waste from our environment and to improve the properties of concrete. The present work aims primarily to investigate the effect of incorporating date palm waste fibers into sand concrete on the properties of this concrete in the fresh and hardened states. For this, two DPF contents were considered. First, 0.1% of DPFs with lengths 2cm and 6cm, and second 0.2%of DPFs with lengths 2cm and 6cm. Furthermore, a factorial design was used for the purpose of analyzing the influence of varying these two parameters, i.e. fiber content and fiber length, on the physico-mechanical properties of the sand concrete produced. In addition, it should be noted that the response to be considered in this design is the compressive and flexural strengths. Moreover, the JMP statistical software was utilized for analyzing the interactions observed and examining the responses that is predicted by the model generated using the factorial design. The findings showed that the expected responses obtained from the adopted model are in good agreement with the experimental data. Further, it was found that the fiber length factor has a positive effect on the response (strength). However, increasing the DPF content in the formulation of sand concrete has a negative effect on his compressive strength.

Legal Education for Civil Engineers in the Context of Bangladesh

As difficult as the work of a civil engineer is, so too are the current regulatory requirements for building projects. However, it is often believed that legal expertise and knowledge are not at all required for the field of civil engineering. But legal issues related to neighboring, environmental, and professional engineering laws, as well as civil and public construction legislation, are frequently raised over the course of construction projects. Though civil engineers are less involved in legal aspects of building than architects and construction managers are. However, several laws and regulations also have an impact on the civil engineers' daily work lives. To find out the necessity if legal education for Civil Engineers several syllabuses of Civil Engineering department of different universities have been studied along with a survey to project engineers has been done to find the usage of Legal aspects. According to the survey report and analysis of syllabuses it’s being found that not all but some vital legal educational portions are used in Civil Engineering works. To prevent legal problems that could lead to a financial or personal disadvantage, it is essential to have a basic awareness of the legal system of one's native country, the legal connections in building projects, and pertinent legal sources. This paper indicates the need for legal education for civil engineers in Bangladesh. It also indicated the global need to educated civil engineers in law that is related to them.

Exploring IoT Integration for Innovative Advancements in Civil Engineering

Civil engineering developments have allocation with the recent trends of integration with IoT technologies and devices. This technology significantly improves automation and remote monitoring for various tasks in the construction process, building maintenance and safety, allowing operators to remain remote free ever than before. IoT is simply the connection of several devices to the internet as per the need. The real implication of IoT is beyond and complex than this. The IoT is meant to signify the establishment of entire networks of devices which are connected to the internet and with each other. Various sensors and monitors recognize the conditions, communicate them to online network or app, and transmit relevant information to your phone automatically. Internet of Things (IoT) is the technology that transforming construction industries, automate processes, and improves construction area and other relevant fields in civil like Global Positioning System (GPS), Water Resources Management and Water Supply, Smart Construction, Water & Waste Water Treatment, Traffic Management and Control, Waste Management, Concrete Technology, Environmental Impact Assessment & Monitoring and Smart Lighting System. This research paper covers implementation of IoT in all these fields of civil engineering and updating the current scenario. IoT is potentially used to increase the productivity, on-site safety, and operational efficiency. Through the placement of low-power sensors, managers can improve worksite visibility at every stage of a project in realtime, from planning to construction, and even task post-construction. The construction industry is bringing real-time information into processes that are long-standing. Internet of Things (IoT) devices and sensors are collecting job site data in a more affordable, efficient and effective way than can ever previously imagined. This latest technology represents exciting hikes forward in the civil engineering world. Foremost principles among these are integrated in this study for Internet of Things with the world of civil engineering.

Fiber reinforced polymers in pervious concrete - a state of the art review

Porous concrete, entitled also as permeable or pervious concrete, is a type of concrete that has interconnected voids that allow water and also air to go across it. It is widely used in the construction of pavements, parking lots, and sidewalks, among other applications, because of its ability to reduce stormwater drainage, better quality of the water, and decrease urban heat island effects. The addition of fibers to porous concrete has been the subject of numerous studies in recent years, with researchers examining the effects of diversified types and amounts of fibers on the material’s properties. Fiber reinforcement can enhance the strength, durability, and ductility of porous concrete, making it more suitable for use in structural applications. Polymer fibers, in particular, have shown promise in improving the properties of porous concrete. They can increase the material’s resistance to cracking, improve its flexural strength, and enhance its heat-absorbing property. However, the characteristics of polymer-reinforced porous concrete are not yet fully understood, and more research is needed to determine the optimal types and amounts of fibers for specific applications. Despite the potential benefits of fiber-reinforced porous concrete, there are also some challenges associated with its use. The addition of fibers can increase the material’s cost and complexity of production, and it may also affect its permeability and drainage capacity.Fiber-reinforced porous concrete represents an exciting area of research and development in the field of civil engineering. While the addition of fibers can enhance the material’s properties, more research is needed to determine the optimal types and amounts of fibers for specific applications. By addressing these challenges and continuing to refine the material’s properties, fiber-reinforced porous concrete has the potential to expand its use in a large spectrum of applications, including structural engineering, transportation infrastructure, and environmental protection.

Nonlinear Forced Vibration Analysis using ‘Elmer’ FEM Package to Develop Poincaré Map and Correlation Method-based Damage Indicators

With the rapid advancement of sensor technology, structural health monitoring (SHM) is also blossoming with prowess in the field of civil, mechanical and aerospace engineering. It deals with the initiation of damage and its characteristic behavior in a deterministic or predictive way. The time-dependent fluctuation associated with the dynamic motion of structures was addressed with few efficient indicators in the past to identify the presence of damage as well as to localize the faulty area. The formulation of all those indicators is validated on specific structural arrangements with certain assumptions. This limitation is considered in this article to project the global applicability of the method along with the inherent constraints. The damage detection in the time-domain is considered to utilize direct accessibility of responses from the sensors attached to the members. It reduces the computational difficulty in transforming response to another domain and also associated approximations that emerge through numerical calculations. Broadly, two types of methods are discussed in this article along with modelling and analysis procedure; methods developed from cross-correlation of response and graphical method or Poincaré map. The model of cantilever beam and axial bar is developed in FreeCad where damage is induced by reducing either thickness or Young’s modulus of damaged elements to simulate cracked member. Meshing of member is done in Gmsh. To generate response history of state variables a newly developed finite element (FE) analysis software package Elmer is then employed for the dynamic analysis of the member with geometric nonlinearity. At last, application of Teager energy operator (TEO) in noise reduction is explained with a general example.

Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)

Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.

Durability assessment of GFRP bars embedded in UHP-ECCs subjected to an accelerated aging environment with sustained loading

Ultra high-performance engineered cementitious composites (UHP-ECCs) have garnered significant attention in the field of civil engineering primarily due to their exceptional tensile strain-hardening performance and excellent compressive strength. To further enhance their tensile strength, particularly in meeting the rigorous environmental and mechanical demands frequently encountered in marine environments, fiber-reinforced polymer (FRP) bars can be employed as the internal tensile reinforcement for UHP-ECCs (referred to as “FRP-UHP-ECC members”). While numerous studies have focused on the durability of FRP bars in concrete environments, the durability of FRP bars embedded within UHP-ECCs, especially with sustained loads, remains unexplored. Glass FRP bars are commonly utilized in civil engineering due to their cost-effectiveness and favorable mechanical properties. Therefore, this paper investigated the durability performance of GFRP bars made from three different matrix resins (i.e. epoxy, vinyl ester and polyester). These bars were centrally embedded in UHP-ECCs and subsequently immersed in alkaline solutions at elevated temperatures while under sustained loads. Their tensile properties over a specified period were evaluated after exposure. The test results revealed a larger degradation in polyester-based GFRP bars compared to vinyl ester-based and epoxy-based bars. In addition, GFRP bars with UHP-ECC covers exhibited less degradation than the bare bars, but this protective effect diminished under sustained loads. Scanning electron microscope (SEM) and X-ray computed tomography (CT) analysis demonstrated that the degradation of GFRP bars was primarily attributed to matrix hydrolysis, which led to a reduction in stress transfer efficiency between fibers, contributing to the tensile strength reduction in GFRP bars.

Response analysis of the vibro-impact system under fractional-order joint random excitation

As a kind of good damping material, viscoelastic material is widely used in machinery, civil engineering, and other fields. In this paper, the viscoelasticity of the system is described by fractional differentiation. The dynamic response of a unilateral vibro-impact system with a viscoelastic oscillator under joint random excitation is studied, in which joint random excitation is composed of additive and multiplicative white noise. The fractional-order derivative was calculated based on Caputo’s definition, and the fractional derivative was equivalent to the corresponding linear damping force and linear restoring force. As a result, a new random system without fractional-order terms was obtained. A non-smooth transformation was introduced, which was equivalent to the original system to a new system without a velocity jump. The steady-state probability density functions of fractional-order vibro-impact systems under joint random excitation are solved by using the random average method and non-smooth transformation. In addition, the effects of parameters on the steady-state response of the system are analyzed.

The influence of basalt fiber on the mechanical performance of concrete-filled steel tube short columns under axial compression

With rapid economic and social development, both concrete-filled steel tube (CFST) composite structures and basalt fiber (BF) have been widely applied in the field of civil engineering. To investigate the laws and characteristics of the influence of chopped BF on the mechanical properties of CFST columns and further promote the application of BF in CFST structures, the axial compressive bearing capacity test of 18 CFST short columns was carried out, and the influence of BF of different lengths on their structural mechanical properties was analyzed. The test results were compared with the theoretical calculation results and the finite element analysis results to verify the reasonableness of the test results. The results reveal that the axial compressive bearing capacity of the CFST short column after adding BF is significantly improved compared to the ordinary CFST short column, in which the bearing capacity and the ductility coefficient are increased by approximately 8.1% and 31.6%, respectively, on average. In addition, changing the length of BF has less effect on the bearing capacity of CFST short columns, the rate of increase in bearing capacity decreases with an increase in the steel ratio of CFST, and the coefficient of ductility increases with the increase in the steel ratio.

OCCUPATIONAL SAFETY AND HEALTH (K3) BUDGET FOR CONSTRUCTION IN ACCORDANCE WITH THE OMNIBUS LAW

This paper is prepared as one of the requirements for completing studies in the field of Civil Engineering. The topic we discuss in this paper is the Occupational Safety and Health (K3) Budget for Construction in accordance with the Omnibus Law. Our objective is to gain a comprehensive understanding of the Construction K3 Budget that aligns with the Omnibus Law in Indonesia and to analyze its impact on the construction industry in the country. The background of this topic is crucial considering the importance of understanding and implementing Occupational Safety and Health (K3) in the construction industry in Indonesia. With the changing times and technological advancements, K3 has become a critical aspect of carrying out construction activities. Therefore, we find it essential to explore and deepen our understanding of the K3 Budget to provide a comprehensive understanding to the readers. In this paper, we will delve into the K3 Budget and the laws regulating it. We will elaborate on the concepts, theories, and principles related to Construction K3. Additionally, we will involve analysis and a review of relevant literature to support our understanding and arguments. We hope that this paper can be beneficial and make a meaningful contribution to both the professional and academic realms. We also aspire for this paper to serve as a useful reference for readers interested in further studying the Construction K3 Budget. In conclusion, we express our gratitude to all those who have assisted and supported us in preparing this paper. Without their help and support, this paper would not have come to fruition. We hope that this paper proves to be useful and provides valuable insights to the readers.

Enhancing Structural Resilience: A Comparative Study of G+35 Storey Irregular Buildings with Bracing and Diagrid Systems

Abstract: The study presented herein delves into the structural performance and behavior of a building subjected to different configurations, namely, an Irregular Building, an Irregular Building with Steel Bracing, and an Irregular Building with Diagrid System. The focus of the investigation is on key structural parameters such as node displacements and storey shear values across various levels of the building. Understanding how these parameters vary in response to different design strategies is vital for assessing the overall stability, safety, and efficiency of the structure in the face of external forces. Structural engineers and designers are constantly striving to optimize building designs to meet safety standards and performance criteria. The incorporation of additional elements, such as steel bracing or a diagrid system, can significantly impact a structure's ability to withstand lateral forces, ensuring resilience in the face of dynamic loads such as wind or seismic events. By comparing the performance of the three configurations, this study aims to provide insights into the efficacy of these design strategies and their influence on key structural indicators. Through a comprehensive analysis of node displacements and storey shear values at various levels, this study seeks to contribute valuable information to the field of structural engineering. The findings may inform future design decisions, guide improvements in structural systems, and enhance the understanding of how different configurations influence the overall behavior of a building. Ultimately, the knowledge gained from this study can contribute to the advancement of resilient and efficient structural designs, with implications for the broader fields of architecture and civil engineering.

Civil engineering and disaster resilience: A review of innovations in building safe and sustainable communities

Civil engineering plays a pivotal role in ensuring the safety and sustainability of communities, especially in the face of increasing natural and man-made disasters. This paper provides a comprehensive overview of innovations in civil engineering that contribute to disaster resilience and the creation of safe, sustainable communities. As the frequency and intensity of disasters continue to rise globally, adopting advanced engineering solutions that mitigate risks and enhance infrastructure resilience is imperative. This review explores cutting-edge technologies and civil engineering methodologies that address disaster-prone regions' challenges. It encompasses a range of innovations, from resilient structural designs and materials to advanced geotechnical engineering practices. The integration of smart technologies, such as sensors and monitoring systems, is discussed as a means to provide early warning systems and real-time data for disaster response. The paper also delves into sustainable practices within civil engineering, emphasizing the importance of eco-friendly construction materials and energy-efficient designs. The role of urban planning and land-use management in disaster risk reduction is highlighted, emphasizing the need for resilient infrastructure that can withstand diverse environmental threats. Furthermore, the review explores case studies and success stories from around the world, showcasing instances where innovative civil engineering practices have effectively minimized the impact of disasters and contributed to the creation of resilient communities. Lessons learned from these experiences offer valuable insights for future projects and policy development. This paper underscores the critical intersection of civil engineering and disaster resilience, providing a foundation for further research and development in creating safer, sustainable communities. By embracing these innovations, the field of civil engineering can contribute significantly to building a resilient future in the face of an increasingly uncertain world.

Evaluating the load-carrying performance of single-bolted connections of inorganic-bonded bamboo composite

ABSTRACT In the field of civil engineering, the utilization of bio-based composites is a viable solution for promoting environmentally friendly practices. A novel bio-based inorganic-bonded bamboo composite (InorgBam) prepared with roller-pressing impregnation, cold pressing, and molding technology was first introduced, and 72 double-shear specimens were tested to investigate the load-carrying performance of InorgBam single-bolted connections. The failure characteristics of the InorgBam single-bolted connections were evaluated using the European yield model (EYM). By incorporating the existing bilinear and Attiogbe models, the load-displacement relationship of the InorgBam single-bolted connections was foretasted with precision. The effect of bolt diameter and component thickness on load-bearing capacity was compared, and relevant design suggestions were proposed. Prediction equations of load-bearing capacity for timber connections in existing standards were briefly reviewed and compared with the test results to illustrate their applicability for InorgBam connections. Comparison results indicated that the equations for timbers exhibited an adequate safety redundancy for the InorgBam. Finally, a simplified equation was proposed based on nonlinear fitting that aims to provide a convenient approach to design activities for InorgBam bolted connections and promote the structural application of InorgBam material.

An acoustic emission onset time determination method based on Transformer

Acoustic emission (AE) technology, as the main method of non-destructive testing technology, has been widely used in structural health monitoring (known as SHM) in the fields of machinery and civil engineering. Locating the failure source is an important application of SHM, and accurately identifying the moment when the AE signal first reaches the sensor (which is called onset time) is of vital importance. Deep learning model has been widely used in onset time determination of AE signals in recent years due to its powerful feature extraction ability. However, as one of the most popular models, Transformer has not been further studied in such field and its effectiveness remains to be proven. In this paper, a novel AE onset time determination method based on Transformer is proposed. Firstly, a preprocessing method based on segmentation-concatenation is applied to divide original data into several connected small segments, while the integrating labeling method is applied on small label segments. Secondly, the preprocessed data and labels are substituted into the Transformer model for training. Finally, for the sequence processed by the Transformer model, the first-time index that reaches the maximum value is obtained as the determination result. Based on the Hsu-Nielson source AE data, the feasibility and performance of this method are analyzed and compared with several commonly used methods: Akaike information criterion (AIC), short/long term average combined with AIC (STA/LTA-AIC), floating threshold (FT) and 1D-CNN-AIC method. The results show that the proposed method is significantly better than AIC, STA/LTA-AIC and FT. Moreover, the determination efficiency is greatly improved while the performance of the proposed method is close to that of 1D-CNN-AIC. Meanwhile, the method has robust performance especially in low signal-to-noise ratio scenario. In practical applications with small-scale data, the proposed method is of relatively high reference as well as application value.

GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine for accurate detection of asphalt pavement cracks

Asphalt cracks are the initial indication and major pavement structural distress in the field of civil engineering because it might potentially intimidate road traffic and highway safety. Managing and maintaining pavement structures are the two crucial issues faced eventually by road administrators. It is essential to take immediate action regarding pavement structural damages to prevent its severity. In recent times, there established numerous deep learning-based image processing methods to detect pavement cracks, but due to the presence of interference factors such as noises and road markings in the images, those methods failed to provide high accuracy and efficiency. Therefore, with the aim to yield high accuracy of crack detection, this article designs a novel asphalt pavement crack identification system “GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine” (GNet TL with IGT-KELM). The road crack images used for perusal are acquired from NHA12D dataset, in which it consists of 40 asphalt pavement images with two different viewpoints. For the purpose of data balancing, some non-crack images are gathered by field survey. The images with large noise distortions and unwanted background information influence crack detection performance so that the preprocessing steps are executed before applying it to the prediction system. The most significant crack and non-crack features from the images are extracted using GNet TL model. With the extracted features, the KELM is well-trained to detect cracks and non-cracks separately. To increase crack detection performance of the classifier, an improved gorilla troop optimizer is introduced to optimize the KELM parameters. The experimental finding reveals that the proposed detection mechanism achieves crack recognition accuracy of about 98.93%, which is considerably greater than compared baseline approaches.

A multispectral vision-based machine learning framework for non-contact vehicle weigh-in-motion

The present study proposes an automated non-contact weigh-in-motion framework using multispectral vision and machine learning method, which provides an all-weather monitoring of heavy vehicles on transportation infrastructures. Multispectral based computer vision techniques consist of two steps, one is acquiring the tire deformation parameters from thermal imaging camera and the latter is obtaining the sidewall characters from the ordinary one. A novel edge detection algorithm is designed for thermal image-based tire deformation estimation. A general recognition model with well-established tire characters database for universal tire sidewall markings, such as non-standard fonts or non-text characters, curved markings or inverted markings, and is developed based on Efficient neural network (E-NET) and YOLOV5S methods. A machine learning-based model for estimating the various vehicles tire-roadway contact forces is established based on eXtreme Gradient Boosting (XGBoost). A database of different vehicles’ tires with a total of 1127 samples from 19 types of tires and 648 of laboratory datasets from common vehicles are collected to train the model. The Bayesian optimization is employed to capture the optimal model, and a hybrid training strategy including biased data is applied to update the model. An importance analysis for 11 features is conducted to validate the performance proposed model. The accuracy, feasibility, and effectiveness of the proposed method is carefully investigated by field experiments for trucks and sport utility vehicles under static and low-speed drive. A maximum of 5 % difference is witnessed by comparing the results with that of the traditional WIM system. The present method presents a cost-effective means with no sensors on structures for vehicle weighting, that demonstrates a great potential to implement in toll station, highway infrastructures as well as health monitoring of civil engineering fields.

Spatial Design Thinking in Coastal Defence Systems: Overtopping Dikes in Southend-On-Sea

Coastal dikes have been built for millennia to protect inhabited lands from exceptional high tides and storm events. Currently, many European countries are developing specific programs to integrate the construction of new dikes (or the raising of existing ones) into the built environment to face sea level rising. Technical difficulties in succeeding in this operation are questioning the paradigm of protection for the long term, pointing out the need for alternative strategies of adaptation that are not yet fully explored. This paper elaborates on innovative models to deal with coastal flooding, presenting the results of an interdisciplinary research and design process for the case-study of Southend-on-Sea (UK). Detailed numerical simulations are used to develop a spatial strategy to accommodate water during extreme events, introducing different prototypes of dike designs that include seawalls, enhanced roughness through rock and stepped revetments, as well as vegetation. The overall goal is to push forward the traditional approach of planning water protection infrastructure within the solely field of civil engineering. It elaborates on the integration of the disciplines of spatial design and engineering and presents novel advances in terms of spatial design for the revetment of overtopping dikes.

A review on PZT in civil engineering

Civil engineering is not just about construction of buildings, towers, bridges etc. but also of their caring and maintenances. For this purpose all the constructed structures have to be monitored for their structural health in terms of damage detection, severity of damage etc., collectively called structural health monitoring (SHM) of structures in civil engineering. For SHM, electromagnetic impedance (EMI) technique is one of the best methods of NDTs being used in the field of civil engineering. EMI uses lead zirconate titnate (PZT) as its piezoelectric component for the purpose of measurement. PZT based devices are widely used in civil engineering. There is a huge demand for health monitoring now a days and to full fill those demands of health monitoring, PZT is one among the most reliable and cost efficient material. Piezoelectric sensor can be used to measure changes in acceleration, strain, pressure and to ensure the better safety measures. These PZT based sensors can be used for predicting the natural disasters like earthquake that helps to save the thousands of lives and can decrease property lose. The piezoelectric sensors can be used to monitor the civil structure which helps an engineer to analyses the characteristics of that structure. It is also used for collecting the data of force that is being exerted on the ground by the building, temperatures and pressure differences in environment etc. Damage detection is a technique that is used to monitor the civil structure, this can improve safety and ensure durability, and for concrete structures, damage detection plays an important role. For the purpose of SHM and damage detection PZT based sensors can be used. For civil engineering applications, PZT-cement based composites have been developed. The concrete and host structure such PZT-cement based composites shows better matching when compared to normal or other piezoelectric ceramics. In this review work we have discussed about SHM/Damage detection, Vibrational control, PZT embedded cement composites and PZT embedded in concrete.

Bandgap analysis of partial-interaction composite beams periodically attached vibration absorbers

Elastic metamaterials with local resonant components exhibit unique bandgap behavior that can be utilized to control the vibration and noise of mechanical structures. This study aims to introduce the bandgap characteristics into the vibroacoustic control of partial-interaction composite beams (PICBs) widely adopted in civil engineering, aviation, and marine fields. Vibration absorbers are arranged periodically on the beam to generate a bandgap in PICBs, thereby transforming a conventional PICB into a metamaterial beam. An analytical approach is proposed to establish the spectral element (SE) matrix of a metamaterial beam based on the Timoshenko–Ehrenfest beam theory. Specifically, the governing equations of motion for PICBs follow Hamilton's principle. This model considers the effects of shear slip between two-layer sub-beams, shear deformations, and rotary inertia. The SE matrix of PICBs is then constructed and applied to obtain the SE matrix of the metamaterial beam by assembling the SE matrix of the vibration absorbers. Subsequently, the wave finite element (WFE) method is employed to predict the dispersion curve of the infinite metamaterial beam and the dynamic response of the finite metamaterial beam. In a benchmark example, the complex band structure and transmission indicated by the proposed approach are validated compared to those calculated by evaluation formulas of the literature and the finite element method. Lastly, the bandgap behavior for the vibration absorber parameters, lattice constants, and shear connector stiffness is investigated based on the defined dimensionless parameters. These results are valuable for optimizing vibration and noise reduction in PICBs equipped with attached vibration absorbers.