Civil Engineering Structures Research Articles

Crack Detection of Structures using Artificial Intelligence System

Abstract: Structural Health Monitoring (SHM) having wider scope of operations such as structural audit, rehabilitation of structures and along with it a major scope of crack detection is also included. Effective crack detection is vital for ensuring the safety and longevity of civil engineering structures. Crack Detection in existing methods is tedious process, it requires costly equipment’s as well as time consuming, and inaccuracy in mechanical equipment’s. The technological advancements especially in the field of Artificial Intelligence (AI) maybe an acceptable solution for crack identification through mobile app using Image processing techniques. The mobile app can be used to accurately identify crack pattern and measure the size of cracks in structures (concrete and steel). The app needed to capture and process data from diverse sources, such as images, in real time. It allows for real-time monitoring of structures and ensures safety and integrity of it. The image processing algorithm is used for to accurately identifying various types of cracks in different lighting, environmental conditions, effective and optimized solution. This project explained about real-time data processing for crack detection and its challenges. This demanded robust backend infrastructure and efficient data processing techniques to deliver prompt and accurate results. User Interface and Experience developing an intuitive and user-friendly interface for the app while maintaining high performance.

Influence of Empirical and Dynamic Periods on the Seismic Responses of Reinforced Concrete Buildings Braced by L-Shaped Shear Walls

The Algerian Seismic Regulation of 1999, version 2003 (RPA99/v2003), presents ambiguities and a lack of thorough explanations in certain articles, which has led to a diversity of readings and interpretations among various stakeholders in the field, particularly the control offices. This situation has caused significant disagreements, especially during the approval of civil engineering files, thus resulting in a negative impact on project progress. To ensure adequate seismic protection of civil engineering structures, this work aims to unify the interpretations of certain passages of the regulation, with a particular emphasis on verifying the fundamental period of the structure, the shear forces at the base, and inter-story displacements using seismic calculation methods prescribed by the RPA. The comparative study demonstrates that no correlation between the period determined by the equivalent static method and that calculated by the spectral modal analysis method has been observed. This justifies the conclusion that adjusting between the empirical period and the dynamic period is not necessary.

The use of concrete slurry waste to improve the properties of the stabilised soil

It is often necessary to use stabilising additives during construction to improve the resulting soil properties to ensure the good quality of civil engineering structures. These additives are often produced at high temperatures and with a high carbon footprint. This paper focuses on soil stabilization with waste concrete slurry, which is produced during the production of fresh concrete. The selected clay soil was stabilized with cement and CSW at 2, 4, and 6 % by weight of soil. Compressive strength, the resistance to freezing and thawing, Initial bearing index (IBI), and California bearing capacity (CBR) were monitored. The research did not confirm better performance of CSW binder even at higher dosage compared to cement, which was due to the high water content of the mixtures. Positive results were achieved especially in Freez-Thaw resistance.

An appraisal of the LQI as an approach to setting target reliabilities in ISO 2394:2015

The idea of coupling economics and safety into one optimization exercise, and hence deriving target reliabilities explicitly from socio-economic consequences of limit state violation, appeared in the JCSS Probabilistic Model Code around the turn of the century; the effort culminated in the form of a Life Quality Index (LQI)-based marginal lifesaving costs (MLSC) methodology in ISO 2394:2015 for determining maximum acceptable failure probabilities, pT, in life safety limit states for civil engineering structures across all nations. Unfortunately, while the methodology does yield adequate levels of safety when applied to structures in the developed countries, the recommended values of pT turn out to be one to two orders of magnitude higher, and unacceptable to every known standard in the world including its own earlier edition (ISO 2394:1998) when applied to structures in the developing world (exemplified by India) with identical functions and expected fatalities. This arises from two shortcomings: (1) an MLSC approach is generally unable to provide an independent constraint on monetary optimization, and (2) the LQI-based measure of MLSC constraint is strongly dependent on a country’s per capita GDP (g) and the resultant pT is effectively governed by the reciprocal of g. This paper recommends continuing with the absolute − not marginal − consequences of violating life safety limit states, and setting constraints on monetary optimization that are consistent with fatality risks from engineering activities more universally accepted to be safe.

Experimental study and multi‐scale refinement model of high damping acrylic polymer matrix VEDs for civil structural seismic retrofit

AbstractThe viscoelastic dampers (VEDs), which can provide both stiffness and damping, have been recently introduced into the field of structural vibration control for seismic enhancement of civil engineering structures. In this study, a kind of high damping acrylic polymer matrix VEDs (HDPVED) is developed independently, and this innovative HDPVED can solve the significant problem of service performance under medium‐high temperature environments, as well as low‐frequency vibration control under earthquake actions for civil engineering structures. To systematically investigate the influencing rules of frequency, temperature, and displacement amplitude on the mechanical properties and damping dissipation performance of HDPVEDs, a series of dynamic mechanical performance tests of the developed HDPVEDs are carried out at different frequencies, temperatures, and displacement amplitudes. The research results show that HDPVED exhibits excellent damping dissipation capability and adaptability under medium‐high temperature environments and low‐frequency excitations. The mechanical properties and energy dissipation performance present a strong correlation with frequency, temperature and displacement amplitude, and there is an obvious coupling effect between the three influencing factors. Based on the macroscopic mechanical property research of HDPVED, the microscopic damping mechanism and microscopic mechanical properties of HDPVED are then investigated. High‐order fractional derivative fraction Voigt and Maxwell model in parallel (FVMP) models are preferred to characterize the combined hyper‐elasticity and viscoelasticity owned by networked molecular chains and free molecular chains. The breaking and reconstruction theory of microphysical bonds is used to assess the effect of packing particles, and the time‐temperature equivalence principle is introduced to assess the effect of temperature. The multi‐scale refinement model is proposed, and the validity and accuracy of this model are verified by testing data of HDPVED. The study results show that the proposed model can accurately describe the effects of frequency, temperature, displacement amplitude, and microstructure on the multi‐scale mechanical properties of HDPVED. It provides a theoretical basis for the multi‐scale design and development of high damping VEDs.

A unified hybrid micromechanical model for predicting the thermal and electrical conductivity of graphene reinforced porous and saturated cement composites

Graphene fillers have gained widespread attention as functional reinforcements for cement composites due to their excellent physical properties. The prediction of the thermal and electrical properties of graphene reinforced cement composites (GRCCs) is of great importance for developing intelligent and multifunctional civil engineering materials and structures. In this current work, a unified hybrid micromechanical model combining effective medium theory (EMT) and Mori-Tanaka-Benveniste (MTB) is developed to simultaneously predict the thermal and electrical conductivity of the GRCCs with considering pores and saturation, in which mechanisms including phonon transport, electron tunnelling and Maxwell-Wagner-Sillars (MWS) polarization are incorporated. Graphene nanoplatelets (GNPs) reinforced cement composites (GNPRCCs) samples are prepared and tested to validate the developed unified model. The results show that the electrical conductivity of the GNPRCCs is more sensitive to saturation than the thermal conductivity. When the porosity n = 0.2 and the GNP concentration is 1 wt%, the relative thermal and electrical conductivity increases by 5 % and 193 %, respectively, when the saturation index increases from 0.6 to 1. Elongated pores are more favorable for the formation of ionic networks for electrical conductivity in the saturated GNPRCCs, while spherical pores are more beneficial for heat transport. Elongated graphene fillers are preferred for enhancing both the thermal and electrical conductivity of dry and saturated GNPRCCs. The work is envisaged to provide guidelines for developing multifunctional cement composites.

INVESTIGATING SHEAR STRENGTH CHARACTERISTICS OF COHESIVE SOILS: STATISTICAL ANALYSIS IN KPK, PAKISTAN

It is crucial to determine the shear strength parameters (Cohesion (C) and Angle of internal friction (Φ) ) in order to encounter soil stability issues such as slope stability, bearing capacity and lateral earth pressure on retaining structures. The shear strength of cohesive soils is an essential parameter in Geotechnical Engineering design and construction. In Khyber Pakhtunkhwa (KPK), Pakistan, the soil is predominantly cohesive. it is essential to evaluate these parameters accurately to ensure safety and stability in civil engineering structures. In this research an effort has been made to evaluate statistically shear strength parameters of cohesive soils in KPK Pakistan. Engineers and construction experts can use the study's findings to influence judgments about the design and building of structures in the area. It highlights the importance of conducting proper soil testing and analysis before designing and constructing any engineering structure in the region. The study was conducted by collecting soil samples from different locations in KPK including Peshawar, Charsadda, Bannu, Swabi, Nowshera, Swat, Abbottabad, Haripur, Lakki Marwat, Tribal Areas, DI Khan, Mardan, and Kohat. The results of the statistical analysis indicate that the Cohesion(C) and Angle of Internal Friction (Φ) in KPK vary significantly across different locations, they are also presented in map of KPK showing values of Cohesion and Angle of Internal Friction using QGIS software. The average values of Cohesion (C) range from 12-47 kPa while mean values of Angle of Internal Friction (Φ) range from 16.2 – 41.1 degrees. The Standard Deviation & Coefficient of Variation was relatively high, indicating significant variation within each location.

Use cases of a digital twin bridge in operation

AbstractThe digital twin bridge makes an important contribution on the way to predictive life cycle management. The potentials are many, especially in the life cycle phase “operation.” There, the digital twin bridge can serve as an efficient tool in terms of analyses, predictions, control, and monitoring. The goal is optimized maintenance, the importance of which continues to grow in view of the current challenges for civil engineering structures. It is therefore particularly important that relevant stakeholders with their needs and requirements are involved in the conception of the digital twin bridge at an early stage. This approach is being pursued by the German Federal Highway Research Institute (BASt) in various research projects that already cover partial aspects of the digital twin and is also being continued in an ongoing project to develop an overall concept for the digital twin bridge. Possible use cases were collected and assigned to the topics of “operational processes,” “maintenance planning and implementation,” and “strategic life cycle management.” In a workshop, the use cases were discussed and initially ranked by and with stakeholders. The results form an important basis for the elaboration of the modular overall concept of the digital twin bridge and the way into practice.

Multiple criteria-based sensor optimization for Structural Health Monitoring of civil engineering structures

Multiple criteria-based sensor optimization for Structural Health Monitoring of civil engineering structures

An Influence of Actuator Gluing on Elastic Wave Excited in the Structure.

In this article, the practical issues connected with guided wave measurement are studied: (1) the influence of gluing of PZT plate actuators (NAC2013) on generated elastic wave propagation, (2) the repeatability of PZT transducers attachment, and (3) the assessment of the possibility of comparing the results of Laser Doppler Vibrometry (LDV) measurement performed on different 2D samples. The consideration of these questions is crucial in the context of the assessment of the possibility of the application of the guided wave phenomenon to structural health-monitoring systems, e.g., in civil engineering. In the examination, laboratory tests on the web of steel I-section specimens were conducted. The size and shape of the specimens were developed in such a way that they were similar to the elements typically used in civil engineering structures. It was proved that the highest amplitude of the generated wave was obtained when the exciters were glued using wax. The repeatability and durability of this connection type were the weakest. Due to this reason, it was not suitable for practical use outside the laboratory. The permanent glue application gave a stable connection between the exciter and the specimen, but the generated signal had the lowest amplitude. In the paper, the new procedure dedicated to objective analysis and comparison of the elastic waves propagating on the surface of different specimens was proposed. In this procedure, the genetic algorithms help with the determination of a new coordinate system, in which the assessment of the quality of wave propagation in different directions is possible.

Performance evolution of epoxy adhesive and CFRP-steel epoxy-bonded joints subjected to hygrothermal exposure

Carbon fiber-reinforced polymer (CFRP)-steel epoxy-bonded joints are widely used in civil, mechanical, and railway engineering structures. However, the mechanical and bonding performance of the epoxy adhesive is sensitive to hygrothermal exposure, which may lead to performance degradation of CFRP-steel bonded interfaces. In this study, the performance evolution of epoxy adhesives and CFRP-steel epoxy-bonded joints after hygrothermal exposure was investigated. Three hygrothermal exposure temperatures (25 °C/50 °C/75 °C) and three hygrothermal exposure times (24 h/120 h/360 h) were considered. The dynamic mechanical behavior and tensile properties of the bulk adhesives were studied by dynamic mechanical analysis (DMA) and tensile tests, respectively. Additionally, the mechanical failure properties of the bonded joints, including failure mode, strain distribution, and bond-slip relationship were obtained by tensile tests. The results indicated that the glass transition temperature (GTT) and tensile strength of adhesive J133 are always higher than those of A2014 in all test scenarios. The interface remains the weakest after hygrothermal exposure. After the 50 °C hygrothermal exposure, both CFRP-steel joints possess the highest bond strength and interfacial maximum bond stress, which corresponds to their higher adhesive tensile strength at the same temperature. In addition, we also find that both CFRP-steel joints show an increase in bond strength after short-term hygrothermal exposure.

Bioengineering solutions for expansive soil stabilization using waste materials: An experimental evaluation

Municipal solid waste incineration ash is the outcome/product of bioengineering science. The incineration or burning of municipal solid waste (MSW) reduces the volume of this biomass by 90%, and the residual ash can be used in several applications. In this study, the potential of municipal solid waste incineration (MSWI) ash and calcium carbide residue (CCR) waste for stabilization of expansive soil has been experimentally evaluated. The abnormal shrink–swell characteristics of expansive soils pose several problems for civil engineering structures when these soils are used for construction in their original form. The study presents an experimental set-up to determine the best combination of additives to achieve the most favorable technical characteristics of expansive soil for civil engineering activities. The percentage of expansive soil has been kept fixed at 60% of the total weight of the mix for varying ratios of stabilizing agents. The optimization of the stabilizing materials is based on the highest strength parameters of the mix achieved for a particular ratio of additives and soil. The two stabilizing agents are waste materials, and their disposal through landfilling has become costly due to a scarcity of space and handling mechanisms. The values of strength parameters, unconfined compressive strength (UCS), and split tensile strength (STS) of a treated mix are found to be highest for a unique combination of 60% lime stabilized expansive soil and 20% MSWI ash and an equal quantity (20%) of CCR. The cumulative effect of adding the best mix of additives to soil and curing time depicts an improvement of 526.03% in UCS and 463.41% in STS of the mix compared to one day and 28 days of curing time. There is scope for further study, such as adding some fibers to the finalized mix to reinforce the soil mix.

Investigate the mechanical behavior of alkali-activated slag mortar using sustainable waste sodium silicate-bonded sand as an alkali activator and carbon free aggregate

Various products generate different types of industrial by-products based on their production characteristics and procedures. Therefore, an accurate classification of these characteristics would contribute significantly to achieving the goals of resource recycling and environmental protection. This study focused on Waste Alkali-Activated Slag Mortar (W-AASM). In the past, inorganic aggregation technology was successfully adopted to replace conventional concrete binding materials and reduce cement usage. Besides utilizing inorganic aggregation technology, the study has introduced a novel industrial by-product known as Waste Sodium Silicate-Bonded Sand (WSSS). WSSS not only serves as a substitute for sodium silicate in alkali-activated agents but also acts as a complete replacement for fine aggregates in mortar. The experimental findings showed that, in addition to fulfilling the original strength requirements of concrete, it results in a significant reduction in cement consumption, minimizing the extraction of natural resources, and decreasing the overall cost of Alkali-activated concrete production. The study highlighted the advantage of WSSS and showed a new dimension of waste foundry sand application in the concrete industry. This method leads to the recycling of waste materials and their optimal use, and as a result, leads to a sustainable ecosystem. The results of this study can be applied to secondary structures in civil engineering and construction projects in the future, such as precast concrete products, concrete pavements, or roads.

Enhancing Concrete Crack Image Detection Using MobileNetV2 and Transfer Learning

The safety of civil engineering structures such as bridges and buildings is paramount, necessitating the early detection and repair of cracks to prevent structural failures. Traditional crack detection methods, including manual inspections and basic image processing, are often hampered by slow detection speeds and limited accuracy. This study explores the integration of advanced deep learning techniques, specifically MobileNetV2, enhanced through transfer learning, to improve the efficiency and accuracy of crack detection. The "Concrete Crack Images for Classification" dataset was employed, featuring 40,000 images from real-world scenarios. Our approach leverages the lightweight architecture of MobileNetV2 and the efficiency of transfer learning to create a robust model that not only reduces computational demands but also achieves high accuracy, with a validation accuracy rate of 99.38%. This paper details the model training, the innovative use of MobileNetV2, and comparative analyses with other deep learning models, demonstrating significant improvements over traditional methods. The findings underscore the potential of MobileNetV2 and transfer learning in practical applications, offering a promising avenue for future research in automated crack detection across various engineering fields.

Geo-Technical Sub-surface Exploration for Delineation of Structural Foundations: A Case Study of Technical University, Ibadan, Nigeria

Sub-surface exploration provides valuable input regarding the type, age and depositional environment of geologic formations existing at a site for use in planning and interpreting the site conditions. Hence, the objective of this paper was to evaluate and provide data on the geo-technical sub-surface exploration for delineation of structural foundations in Technical University, Ibadan, Nigeria using Vertical Electrical Sounding (VES) to determine the suitability of the sub-surface competence of the site for construction purposes and building development in the institution. The results indicated that the resistivity value of different geo-electric layers showed that geologic value greater than 20 m (thick weathered bedrock) can support massive civil engineering structure while 5-10 m can support small civil engineering structure.

Flexural and Bond Behavior of Concrete Beams Strengthened with Carbon Fiber-Reinforced Polymer Sheet

Concerns have been expressed regarding the issue of using conventional steel rebar in civil engineering structures. One contributing factor is the corrosion that occurred on the steel rebar. The corrosion process was not promptly detected due to the gradual reaction time exhibited by the steel, concrete, and environmental conditions. Corrosion problems often manifest when corrosion has progressed to a critical point and damaged the concrete structure. Consequently, to address this issue, this research proposed the application of carbon-fiber reinforced polymer sheet (CFRP). The aim is to measure the flexural load of conventional and FRP-reinforced concrete beams, to test the effects of cracking and deflection on concrete beams strengthened with FRP sheet, and to determine the degree to which these loads damaged these beams. A repair method was implemented by applying two distinct diameters of CFRP: 50 mm × 100 mm and 100 mm × 200 mm. The finding shows that the concrete beam's significantly higher flexural strength resistance was observed for CFRP of greater dimensions. This is mostly because the CFRP can disperse the applied stress more effectively. Alternative applications of fibre-reinforced polymer should be explored for further research to facilitate the development of substitutes for CFRP.

Model updating of a shear‐wall tall building using various vibration monitoring data: Accuracy and robustness

SummaryAcceleration measurements are often used for model updating of civil engineering structures, especially in the case of seismic monitoring. It is yet unclear if accelerations alone would generate an accurate and robust finite‐element (FE) model. This study examines this notion and analyzes the possibility of using other vibration monitoring data for model updating of shear‐wall tall buildings. This study compares the accuracy and robustness of the FE models being optimized via accelerations, roof displacement, wall rotations, interstory drift ratios, and the linear combination of these measurements. A numerical case study is analyzed using Timoshenko beams for modeling the lateral vibration of a benchmark 42‐story building under seismic excitations. Results show that the acceleration response of the examined building is mostly governed by its higher vibration modes. Depending on the characteristics of ground motions, using accelerations alone may generate an FE model biased towards higher‐order modes without effectively capturing the lower‐order modes. For instance, the first modal frequency of the updated FE model could be 12.0% lower than the true value, and the reconstructed displacement and rotation responses are noticeably inaccurate. Employing multi‐source monitoring data for model updating, for example, the combinations of roof displacement and acceleration measurements, could reduce the normalized root‐mean‐square errors in displacements by more than 70%. This study also quantifies the robustness of the FE model under various measurement noise levels and 50 pairs of earthquake records. Finally, the effects of multi‐source data on FE model updating are validated via experiments on a 7‐story shear wall building. Analysis reveals that a more accurate and robust FE model can be determined via a combination of accelerations and top displacement than via acceleration alone.

Innovative Use of UHF-RFID Wireless Sensors for Monitoring Cultural Heritage Structures

This paper reports a novel investigation in applying commercial Ultra High-Frequency RFID tags (UHF-RFID tags), which are widely used in logistics as sensing elements in civil engineering structures, particularly for monitoring out-of-plane displacements of brick masonry walls. Both laboratory tests and in situ experimental tests assessed the feasibility of the proposed application. Laboratory tests showed a very satisfactory response while the in situ experiments showed a weaker response. Nevertheless, the potential reliability of the proposed technique can be stated. The authors traced back the causes of the performance decrease to environmental interference, mainly due to the extensive presence of a rigid steel frame surrounding the out-of-plane loaded panels. Measurements of displacements, in fact, are obtained indirectly from the phase of UHF-RFID signals that strongly suffer from multipath generated by metallic surfaces. Despite some limitations, the proposed measurement technique permits a reliable and sustainable approach to the monitoring of structures. The use of commercial UHF-RFID wireless tags, in fact, assures easy and fast installation operations and assures the possibility of placing a large number of sensors over the structure with very low maintenance costs with respect to the more traditional monitoring techniques. Moreover, using very thin and small commercial UHR-RFID tags on cultural heritage structures can represent an opportunity for sustainable long-time monitoring with reduced costs. Overall, the results of this study are sufficiently satisfactory to be considered as the opening of new possible scenarios in wireless structural monitoring in the civil engineering field. The authors propose as future work to use UHF-RFID tags for the real-time monitoring of an existing masonry facade that, not being characterized by the presence of a steel frame, can potentially assure an adequate response and properly transmit the electromagnetic signal.

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

Identification of damaged member in a truss structure using acoustic emission technique aided by SVM

Structures are prone to damage, and detecting them at their very initiation is extremely important for taking corrective measures. Truss is a very important civil engineering structure having a complex geometry: width and thickness being very small compared to the length of a member and the presence of discontinuities in the form of joints. Most of the existing techniques identify damages after they have grown to a substantial degree. For early detection of damages, the acoustic emission (AE) technique may be used effectively as the initiation of damage itself results in the emission of AE wave, analysis of which may lead to detection of the damage. Additionally, signature behavior of the virgin structure is not necessary, unlike many other nondestructive testing methods. Existing studies largely use the time of arrival (TOA) of AE wave at the sensor location(s) in the formulation for damage localization. However, TOA is significantly affected by reflected waves from the edges of truss members and attenuation of signal strength during its passage through joints. In addition, TOA suffers from a lack of objectivity in prescribing the relevant threshold value. Accordingly, damage localization may be affected. In this context, a machine learning approach is very promising if appropriate signal features are used for training. Accordingly, in the present study, an effort is made to develop a support vector machine model for the localization of a truss member, where damage has been initiated. The model is trained and then tested using different sets of experimental data, collected from a laboratory-scale truss. The results of the localization, as predicted by the model, are found to be encouraging when compared with the physical observation.