Search Results (54)

Reinforced cement concrete (RCC) water tanks are essential for water storage and distribution facilities in every region. The durability and structural integrity of RCC water tanks are crucial to maintaining an uninterrupted water supply to the surrounding areas. This study evaluates the structural integrity and functionality of a water tank in Karaikal, a coastal region in the Union Territory of Puducherry, India, subject to severe exposure conditions characterized by high humidity and temperature variability. An RCC water tank with a capacity of 10 lakh L in Thirunallar, Karaikal, is considered in this study. The methodology for the condition assessment includes visual inspection, non-destructive testing (NDT), and structural analysis in STAAD PRO software. NDT, including the Schmidt rebound hammer test and ultrasonic pulse velocity (UPV) test, was employed to evaluate the indicative compressive strength and in situ quality of an RCC water tank. The structure was modelled using structural drawings obtained from the Public Works Department, Karaikal. The NDT testing findings were incorporated into the model, and the structure was analyzed. Finally, the induced stress from the STAAD Pro model was compared with the in situ concrete compressive strength to assess the tank’s structural safety. The rebound hammer test results indicate that the in situ compressive strength of the tank’s beams and columns ranges from 12 MPa to 43 MPa, and the STAAD Pro analysis shows induced stresses ranging from 2.42 to 10.59 MPa. The comparison shows that the structure has higher safety margins. Hence, the deterioration observed during the visual inspection was not due to a deficiency in structural strength but rather to durability issues caused by environmental distress. Finally, suitable repair and rehabilitation methods were recommended to mitigate the deterioration based upon NDT measurements and the outputs of the structural analysis. Full article

This study assessed the carbonation-related durability of an existing reinforced concrete building in Seoul scheduled for demolition to examine the level of durability performance commonly assumed for building structures. The compressive strength of concrete core specimens was compared with the estimated compressive strength derived from the rebound hammer, showing similar overall trends despite noticeable scatter, indicating that rebound testing can serve as a supplementary indicator when interpreted with caution. Carbonation depth measurements revealed that indoor locations tended to exhibit the greatest carbonation depths, likely reflecting higher CO₂ concentrations associated with occupancy and daily activities, as well as indoor ventilation and moisture conditions. For exterior walls, orientation affected carbonation progress; carbonation depths were greater on the southwest-facing wall than on the northwest-facing wall, suggesting that higher solar radiation may promote drying and facilitate CO₂ diffusion, thereby accelerating carbonation. When the carbonation rate coefficients were compared under similar compressive strength conditions, the southeast-facing wall exhibited a coefficient approximately 1.1 times greater than that of the northwest-facing wall. These results indicate that carbonation cannot be explained by strength alone and highlight the importance of incorporating exposure-related factors (e.g., solar radiation, drying, rainfall, and shielding) into carbonation behavior assessment. Full article

This study aims to present the results from the assessment of carbonation depth, corrosion, and compressive strength of real marine structures in a 49-year-old gas processing industry. The assessment was achieved through visual observations and non-destructive tests, including rebound hammer test, ultrasonic pulse velocity (UPV) test, and potential corrosion mapping, conducted in the field. Several cylindrical samples were also cored to test the concrete compressive strength and carbonation depth. The results were subsequently used to calculate the remaining load-bearing capacity of the structures. The observations and measurements showed that carbonation depth ranged from 0 to 63% of the concrete cover, and potential corrosion was at a low to medium level in areas where corrosion had not occurred, while the actual compressive strength is still above the design strength. Moreover, based on the UPV test, the pulse velocity of the concrete is around 3600 m/s, indicating a good concrete quality. Meanwhile, severe corrosion of reinforcing steel occurred locally and only at certain places, which caused a very significant reduction in the diameter and cracks as well as spalling of the concrete cover. The process further led to a significant reduction in the load-bearing capacity. Therefore, repairing and strengthening of the structures were proposed using epoxy resin with corrosion inhibitor, cementitious, polymer-modified repair mortar containing reactive micro-silica, Carbon Fiber Reinforced Polymer (CFRP) rods, and CFRP sheets. The proposed method can be applied to these structures and also serves as a reference for repairing and strengthening other structures experiencing the same issue. Full article

To investigate the dynamic performance and seismic response of Ming dynasty masonry pagodas in the Jiangnan region of China, the Great Wenfeng Pagoda in Taizhou, Zhejiang Province, was selected as the study object. Based on on-site inspection and maintenance records, the in situ compressive strength of masonry at each level was measured using a rebound hammer, considering that the pagoda was immovable and no destructive testing was permitted. A numerical model of the pagoda was established using the finite element software ABAQUS 2016 with a hierarchical modeling approach. The seismic response of the pagoda was computed by applying the El Centro wave, Taft wave, and artificial Ludian wave, and the seismic damage mechanism, the distribution of principal tensile stress, and seismic weak zones were analyzed. The results showed that the horizontal acceleration increased progressively along the height of the pagoda. Under minor earthquakes, the pagoda remained largely elastic, whereas under moderate and strong earthquakes, the acceleration at the top and bottom and the story drifts increased markedly, with the effects being most pronounced under the Taft wave. The damage was primarily concentrated in the first and second stories at the lower part of the pagoda and around the doorway. Tensile stress analysis indicated that the masonry blocks in the first and second stories were at risk of tensile failure under strong seismic action, whereas the lower-level stone blocks in the first story remained relatively safe due to their higher material strength. This study not only reveals the seismic weak points of Ming dynasty masonry pagodas in the Jiangnan region but also provides a scientific basis for seismic performance assessment, retrofitting design, and sustainable preservation of traditional historic buildings. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

Condition assessment of reinforced concrete structures presents a significant challenge worldwide as structures built in the post-war construction period (1950s–1970s) reach end of service life. The alkali-silica reaction (ASR) is one of several damage mechanisms which commonly affect infrastructure in Canada. Frequent freeze-thaw cycles and heavy use of de-icing salts in winter as well as high heat and humidity in summer are expected to have intensified ASR-induced damage. This work investigates five segments of a pier cap—PC, which had undergone encapsulation repair, and four segments of a pier shaft—PS, which represented dry and semi-submerged conditions, removed from a highway bridge constructed starting in 1957. Preliminary evaluation through visual inspection (conventional, qualitative and quantitative using the cracking index—CI) and non-destructive techniques (rebound hammer—RBH, ultrasonic pulse velocity—UPV and surface resistivity) was conducted on both internal (i.e., cut during decommissioning) and external (i.e., exposed while in service) surfaces of five PC segments and four PS segments. Differences in geometry, exposure conditions and repair history from the two members were found to have limited impact on the results of quantitative tests (i.e., CI, RBH and UPV results with average values of 1.6 mm/m, 37 MPa and 2.4 Km/s, respectively) while still exhibiting qualitative differences in visual determination (i.e., crack patterns, surface appearance and crack widths). Full article

Concrete compressive strength is a critical property for structural performance and construction scheduling. Traditional non-destructive testing (NDT) methods, such as rebound hammer and ultrasonic pulse velocity, offer limited reliability and resolution, particularly at early ages. This study presents an AI-powered structural health monitoring (SHM) framework that integrates multi-type and multi-position piezoelectric (PZT) sensor networks with machine learning for in situ prediction of concrete compressive strength. Signals were collected from various PZT types positioned on the top, middle, bottom, and surface sides of concrete cubes during curing. A series of machine learning models were trained and evaluated using both the full and selected feature sets. Results showed that combining multiple PZT types and locations significantly improved prediction accuracy, with the best models achieving up to 95% classification accuracy using only the top 200 features. Feature importance and PCA analyses confirmed the added value of sensor heterogeneity. This study demonstrates that multi-sensor AI-enhanced SHM systems can offer a practical, non-destructive solution for real-time strength estimation, enabling earlier and more reliable construction decisions in line with industry standards. Full article

Slab-replacement operations are crucial for restoring deteriorated CRTS I slab ballastless tracks to operational standards. This study investigates the structural implications of the operation by evaluating the strength characteristics and material properties of track components both prior to and following replacement. Apparent strength was measured using rebound hammer tests on three categories of slabs: retained, deteriorated, and newly installed track slabs. In addition, samples of old and new filling resins were collected and tested to determine their elastic moduli. These empirical data were subsequently used to develop a refined finite-element model that captures both pre- and post-replacement conditions. Under varying temperature loads, disparities in component performance were found to significantly affect stress distribution. Specifically, before replacement, deteriorated track slabs exhibited 10.74% lower strength compared to adjacent retained slabs, whereas, after replacement, new slabs showed a 25.26% increase in strength over retained ones. The elastic modulus of old filling resin was measured at 5.19 kN/mm, 35.13% below the minimum design requirement, while the new resin reached 10.48 kN/mm, exceeding the minimum by 31.00%. Although the slab-replacement operation enhances safety by addressing structural deficiencies, it may also create new weak points in adjacent areas, where insufficient stiffness results in stress concentrations and potential damage. This study offers critical insights for optimizing maintenance strategies and improving the long-term performance of ballastless track systems. Full article

Continuous SiC fiber-reinforced SiC matrix composites (SiC/SiC), as structural heat protection integrated materials, are often used in parts for large-area heat protection and sharp leading edges, and there are a variety of low-velocity impact events in their service. In this paper, a drop hammer impact test was conducted using narrow strip samples to simulate the low-velocity impact damage process of sharp-edged components. During the test, different impact energies and impact times were set to focus on investigating the low-velocity impact damage characteristics of 2.5D SiC/SiC composites. To further analyze the damage mechanism, computed tomography (CT) was used to observe the crack propagation paths and distribution states of the composites before and after impact, while scanning electron microscopy (SEM) was employed to characterize the differences in the micro-morphology of their fracture surfaces. The results show that the in-plane impact behavior of a 2.5D needled SiC/SiC composite strip samples differs from the conventional three-stage pattern. In addition to the three stages observed in the energy–time curve—namely in the quasi-linear elastic region, the severe load drop region, and the rebound stage after peak impact energy—a plateau stage appears when the impact energy is 1 J. During the impact process, interlayer load transfer is achieved through the connection of needled fibers, which continuously provide significant structural support, with obvious fiber pull-out and debonding phenomena. When the samples are subjected to two impacts, damage accumulation occurs inside the material. Under conditions with the same total energy, multiple impacts cause more severe damage to the material compared to a single impact. Full article

During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve mechanical properties such as compressive strength, workability and durability, and in many of the proposed mixes, the use of industrial waste stands out both for their ability to improve the mechanical properties of concrete and for the importance of their reuse from a sustainability point of view. In this paper, the use of two waste materials, perlite and rhyolite, in concrete mix design was studied in detail, considering their influence on the compressive strength at 7 and 28 days of curing. The waste materials were introduced in the mix design as substitutes for cement in percentages of 15% and 30% in weight. In addition, perlite was micronized to two different particle sizes, 20 μm and 63 μm, respectively, according to what is already used in concrete within perlite in the mix design. The behavior of the structural concrete containing perlite and rhyolite was compared in terms of compressive strength, Young modulus and produced equivalent CO₂ with that of a standard C25/30 reference concrete, and with that of a mix design created using other waste materials, namely fly ash, metakaolin and silica fume, considering cement replacements that are always at 15% and 30% by weight. Moreover, ultrasonic testing and rebound hammer tests were run to evaluate a possible relationship between the physical-mechanical properties of the design mixes and their volumetric and surface characteristics. Full article

Concrete production has significant environmental impacts due to extensive raw material use and high CO₂ emissions. Reusing structural concrete elements can potentially reduce these environmental impacts by reducing the demand for new production. However, reliable and practical documentation of concrete properties is needed for safe and scalable reuse. Although several non-destructive testing (NDT) methods show promise for in situ assessment of concrete properties, a clear gap remains in implementing them into a comprehensive approach for reuse documentation. This study investigates the potential of combining ultrasonic pulse velocity (UPV), rebound hammer (RH), and electrical resistivity (ER) methods for documenting concrete properties for reuse. Several parameters relevant to reuse scenarios, such as saturation level and aggregate type and size, were systematically evaluated to understand their impact on NDT documentation of concrete for reuse. NDT documentation of compressive strength and chloride migration coefficient was assessed on 120 cylindrical specimens. Fifteen concrete mixtures were used with three aggregate compositions and five water–cement ratios. The experimental results are discussed in the context of in situ documentation of structural elements in donor buildings to ensure the practical applicability of the findings. The findings show that these NDT methods can potentially document the properties of concrete reliably and practically, thereby addressing the lack of in situ documentation procedures needed to enable the safe and scalable reuse of structural elements. Full article

This study investigated the mechanical properties of concrete specimens produced with a limestone aggregate through laboratory testing. Destructive tests, specifically concrete compressive strength and splitting tensile strength tests, were conducted. Additionally, the Schmidt hammer rebound hardness test, a non-destructive method, was performed on the same specimens. The experimental results, obtained from varying water-to-cement and limestone aggregate-to-cement ratios, yielded the following ranges: compressive strength from 23.6 to 42.6 MPa, splitting tensile strength from 3.2 to 5.1 MPa, and Schmidt hammer rebound values from 18 to 43 N. The correlation between the non-destructive and destructive test results was analyzed experimentally and statistically. Utilizing the experimental data, statistical models were developed, resulting in equations with a high determination coefficient (R² > 0.95) for accurately predicting concrete compressive and splitting tensile strengths. This approach offers the potential for significant labor and time savings in the production of sustainable conventional concrete that meets relevant standards. Furthermore, it aims to facilitate the estimation of concrete strength in existing structures. Full article

In any rock engineering project, the uniaxial compressive strength (UCS) is one of the most relevant parameters to be determined as it is used for a variety of purposes. Traditionally, the UCS of a rock sample is obtained by carrying out a uniaxial test on a rock core. The UCS can also be estimated indirectly by correlating it with different parameters such as the pulse velocity (V_p), Schmidt hammer rebound number (R_n), effective porosity (n_e), total porosity (n_t), dry density (γ_d), point load index (I_s50), shear wave velocity (V_s), Brazilian tensile strength (BTS), slake durability index (SDI), or by using an artificial neural network (ANN). This paper presents a comparative study for an additional approach to determine the UCS, namely by converting triaxial test results to the UCS. This research has been conducted to encourage further utilisation of laboratory test results obtained from triaxial tests. Triaxial tests are normally performed to determine the intact rock strength envelope. By further utilisation of triaxial test data to estimate the UCS, the database of the UCS can be enriched for rock engineering projects. The method can also be used in cases of limited samples or when samples are too challenged for carrying out a USC test. The research showed that the UCS derived by this method is more direct and more accurate than many empirical methods. Furthermore, the test procedure described herein (carrying out the UCS and triaxial tests on the same sample) can be used to evaluate the accuracy of the calculated UCS. It is important to bear in mind that the scope of this study is not to develop a new method or replace the traditional uniaxial compression test with the method presented in this paper. The purpose and intention is to document that the test results obtained from triaxial testing can be utilised to also provide values of UCS from the same samples. Full article

This paper presents a machine learning (ML) model for predicting concrete strength using a combination of two non-destructive testing (NDT) methods: ultrasonic pulse velocity (UPV) and rebound number (RN). The model was developed using an extensive and diverse dataset and is the first such model to consider the effect of three different sample types: cubic, cylindrical, and core samples. This study is also the first of its kind to present an in-depth analysis of the results to quantify model uncertainty, which is an important prerequisite for its use in practice. Accordingly, two ML models were trained using 620 data points from the aforementioned sample types. The prediction intervals and associated uncertainties of the ML-based approach were thoroughly examined. Validation with the testing dataset showed that 93% of the testing data points for the combined cylindrical and cubic dataset fell within the 95% prediction interval, indicating strong alignment with expected results. Based on the findings, a roadmap is also proposed for future work. Full article

This study aims to accurately predict abrasion resistance, measured through the Los Angeles (LA) abrasion test, and modulus of elasticity, assessed using the Micro-Deval Abrasion (MDA) test, to support structural integrity and efficient material use in construction projects. We applied multi-output machine learning models—specifically Linear Regression (LR), Huber, RANSAC, and Support Vector Regression (SVR)—to predict LA and MDA values based on primary input parameters, including Uniaxial Compression Strength (UCS), Point Load Index (PLI), Schmidt Hammer Rebound (Sh_h), and Ultrasonic Pulse Velocity (UPV). The experimental work involved assessing model performance using metrics such as Mean Absolute Error (MAE), R-squared (R²), and Mean Squared Error (MSE). Linear Regression demonstrated superior predictive accuracy, achieving 94% for R² with an MAE of 0.21 and MSE of 0.09 for LA predictions and 92% for R² with an MAE of 0.24 and MSE of 0.11 for MDA predictions. These results underscore the potential of machine learning techniques in accurately predicting critical material properties, offering engineers reliable tools for optimizing material selection and structural design. This research contributes to the advancement of construction practices, promoting the development of durable and efficient infrastructure. Full article