Plasticity Index Research Articles

Potential of Limestone Calcined Clay Cement (LC3) in soil stabilization for application in roads and pavements construction

Clay soil is associated with geotechnical problems of swelling and shrinkage, which causes deformations on structures. Ordinary Portland Cement (OPC) which is the most common stabilizing agent for clay soils, remains unaffordable in most developing countries, and its production also contributes about 8 % of global anthropogenic CO2 emissions. The present study aimed to investigate the effect of Limestone Calcined Clay Cement (LC3) on the stabilization of clay soils for applications in road construction. Clay soil was mixed with quarry dust to reduce the clay content and save cement. The mixture was stabilized using LC3 and OPC separately in proportions of 1 %, 3 % and 5 %. The effect of stabilizer dosage on the performance of clay soil was studied by monitoring the changes in Atterberg limits, Proctor test and soaked California Bearing Ratio (CBR). The mineralogical and microstructural investigation was carried out using X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and Thermogravimetric Analysis (TGA). Plasticity index (PI), linear shrinkage (LS), and optimal moisture content (OMC) of stabilized soil was found to decrease with increase in the dosage of LC3 and OPC. The Maximum Dry Density (MDD) and soaked CBR increased with addition of both LC3 and OPC. Formation of calcium silicate hydrate (C-S-H) in both LC3 and OPC stabilized clay soil, as well as formation of Hemi-carboaluminate in LC3 stabilized clay soil was responsible for improved properties of the stabilized soil. The performance of LC3 was found to be comparable to that of OPC, at optimum cement ratio of 5 %.

Abundance of Marine Macrodebris on the Northern Coast of Jaffna Peninsula, Sri Lanka.

Marine plastic debris has emerged as a pressing concern along the northern coast of Jaffna, Sri Lanka, posing a significant threat to marine resources. A preliminary study was conducted to investigate the abundance and characteristics of marine plastic debris at four major fish landing sites in the northern coast of Jaffna, using the Clean Coast Index (CCI) and Plastic Abundance Index (PAI). The results revealed that the average abundance of marine debris and plastic debris were 1.71 ± 0.42 items/m2 and 1.66 ± 0.57 items/m2, respectively. The most common types of plastic debris represented plastic rope and net pieces (23.2%), followed by unidentified weathered plastic fragments (16.7%), beverage bottles (16.2%), bottle caps and lids (13%), and styrofoam (14.1%). The recognized sources of plastic debris were mainly fishing-based activities, recreation activities, transboundary sources, and unidentified sources. The fish landing sites were classified as exceptionally polluted, with a CCI exceeding 10 and a PAI value exceeding 8. Myliddy had the highest debris density, indicating substantial pollution levels, followed by Point Pedro, Mathagal, and Valveddithurai. These findings underscore the urgency of establishing a comprehensive plastic waste management framework for the northern coast of Jaffna and devising strategies to address buoyant debris within the northern Indian Ocean. Furthermore, this study carries significant implications for the local marine ecosystem, coastal communities, and Sri Lanka's broader environmental policies and practices.

Treatments Technology and Mechanisms of East Asia Black Cotton Soil

The East Asia black cotton soil (BCS) cannot be used as embankment filling directly due to its high clay content, liquid limit, plasticity index, and low CBR strength (CBR < 3%). This study evaluates the effects of treating East Asia BCS with lime, volcanic ash, or a combination of both on its engineering properties. Experiments were conducted to analyze the basic physical properties, swelling characteristics, and mechanical properties of the treated soil. Results indicate that lime addition significantly reduces the free swelling rate, improves limit moisture content, increases optimum moisture content, decreases maximum dry density, and enhances CBR value. Although volcanic ash also improves BCS performance, its effects are less pronounced than those of lime. The combined treatment with lime and volcanic ash exhibits superior performance, greatly reducing expansion potential and significantly increasing soil strength. Specifically, a mixture of 3% lime and 15% volcanic ash optimizes the liquid limit, plasticity index, and CBR value to 49.2%, 23.8, and 24.7%, respectively, meeting the JTG D30-2015 requirements and reducing construction costs. The treatment mechanisms involve hydration exothermic reactions, volcanic ash reactions, and semipermeable membrane effects, which collectively enhance the soil’s properties by producing dense, high-strength compounds.

Experimental analysis of the cyclic behavior of rammed earth walls reinforced with arundo donax natural fiber

This experimental study analyzes the seismic behavior of rammed earth walls with Arundo donax natural fiber inclusions, also called "caña brava” (REWC), and without inclusions (REW). The experimental program consists of two stages: physical and mechanical characterization of materials, where properties such as particle size, density, Atterberg limits, and compressive and flexural strength were determined; and dynamic tests, which included compressive and cyclic load tests on REW and REWC walls. The walls were subjected to cyclic loading with derivatives between 0.2 % and 1.4 % in-plane combined with a constant vertical compressive stress of 13 kPa to simulate seismic forces in the presence of gravity loads. The results regarding loading, hysteretic response, energy dissipation, stiffness degradation, and failure mode are compared. In addition, the envelope curves of the load-displacement hysteretic responses are analyzed using a capacity spectrum approach. It is observed that the inclusion of Arundo donax natural fiber negatively affects the mechanical properties of the walls and their hysteretic response. It is also observed that the energy dissipation is affected by the inclusion of Arundo donax natural fiber. However, the stiffness behavior is similar in all the walls. Rigid body failure modes are identified in all the walls, but the walls, including Arundo donax natural fiber, form failure planes that divide the wall into two bodies.

Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks

The compression index (Cc) serves as a crucial parameter in predicting consolidation settlement in fine-grained soils, representing the slope of the void ratio logarithmic effective stress curve obtained from oedometer tests. However, traditional consolidation testing methods are notably time-consuming, typically spanning a 15-day period for preparation, execution, and parameter calculation, leading to significant delays in civil engineering projects. Therefore, there is an urgent need for effective methodologies to determine consolidation parameters within a shorter timeframe. Although various empirical formulas have been proposed over the years to correlate compressibility with soil parameters, none have reliably predicted the Cc across different datasets. In this study, to overcome this challenge, an alternative approach using artificial neural network (ANN) methodology to predict the compression index of fine-grained soils based on index properties is proposed. For this purpose, an ANN was trained and validated using a dataset consisting of 560 high and low- plasticity soil samples obtained from construction sites in various regions of Turkey over the last forty years, as well as soil borings in Istanbul. The modeling of artificial neural networks was performed using the Regression Learner program, which integrates with the Matlab 2023a software package and offers a user-friendly graphical interface for AI model development without coding. The data set, which was structured as a matrix with dimensions of 458 × 6, included input parameters such as the natural water content, liquid limit, plastic limit, plastic index and initial void ratio, as well as information on the compression index, which was the output variable. The developed ANN model showed an outstanding predictive performance when predicting the output of the test data, achieving an outstanding R2 score of 0.81. This underlines the potential of ANN methodologies to efficiently extract important data with fewer experiments and in less time, and offers promising applications in the field of geotechnical engineering.

Utilizing Magnesium Carbonate Induced by CO2 to Modify the Performance of Plastic Clay

Highly plastic clays pose significant challenges in engineering projects. Various techniques have been employed to enhance their properties, though many face difficulties related to implementation and environmental impact. This study examines the effect of CO2-induced magnesium carbonate on improving the geotechnical behavior of plastic clay. CO2-induced magnesium carbonate was produced via mineral carbonation and used to improve the behavior of highly plastic natural clay. CO2 gas was injected into a sodium hydroxide solution to produce carbonate ions (CO32−). Magnesium carbonate was precipitated on a laboratory scale by adding magnesium sulfate solution to the carbonate ion solution. Clayey soil samples were obtained from test pits in the Meyghan Plain near Arak, Iran. The clay samples were treated with different percentages of the produced magnesium carbonate. Various parameters of the treated and untreated samples, including index properties, unconfined compressive strength, consolidation behavior, and swelling potential, were studied. It was found that the liquid limit and plasticity index of the treated clay decreased as the magnesium carbonate content increased. The soil classification changed from high plastic clay (CH) to low plastic silt (ML) with the addition of 15% magnesium carbonate to the highly plastic clay. The unconfined compressive strength of the treated clay increased. Additionally, the consolidation behavior and swelling index of the treated clay improved as the magnesium carbonate content increased. This study confirms that CO2-induced magnesium carbonate is a promising material for improving the behavior of highly plastic clays, offering a sustainable approach to environmental management.

IMPROVEMENT OF GEOTECHNICAL PROPERTIES OF A COIR REINFORCED LATERITE

Laterites used mostly for construction in the tropics can sometimes be problematic due to insufficient geotechnical properties. This explores the potential benefits of incorporating coir reinforcement into laterite. Coir, derived from coconut husk fibers is a sustainable, renewable and abundant resource that has high tensile strength, low density, and good resistance to decay. Geotechnical properties such as Liquid limit (LL), Plastic limit, Plasticity index, Maximum dry density, Optimum moisture content (OMC) and California bearing ratio (CBR) of the laterite were determined before reinforcement. The coir was cut into different lengths (3 to 5 cm) and added to the laterite at different percentages (0.25 to 1.5% at 0.25% increment). The geotechnical properties of the reinforced soil were determined and the results were analyzed using analysis of variance and fuzzy logic. The CBR of the reinforced soil was predicted using fiber content, OMC, and LL The precision of the fuzzy logic model was obtained by comparing the model results with the actual experimental results. Addition of fiber at 0.25% was found to be the optimum as it increased the CBR of the soil by 27.24% and reduced the Liquid limit by 15.47%. The fuzzy logic prediction has a RMSE of 1.18, MAPE of 4.68% and R-squared of 0.98 which shows that the fuzzy logic model is satisfactory. The study concluded that coir is a potential reinforcement for improving the geotechnical properties of laterite and that Fuzzy Logic can be used to predict the CBR of coir reinforced laterite.

Utilising the Ipzo parameter for more reliable estimates of undrained shear strength directly from CPTU data in saturated natural clay-like soils

Ramsey and Tho illustrated the ability of the Ipzo parameter to estimate plasticity index in a wide range of saturated natural soils and highlighted the potential of the Ipzo parameter for improving the reliability of estimations of other soil properties affected by plasticity index. In this note, an improved method is proposed for estimating undrained shear strength in clay-like soils using CPTU data, where clay-like is defined as Ipzo ≥ 12%. The improved method is validated using a geographically and geologically diverse “Nkt1-database” comprising 54 anisotropically consolidated undrained triaxial compression tests, with complementary CPTU measurements from 36 geological units at 18 globally distributed marine sites.

RETRACTED: Relationships among fall cone flow index and consistency identifiers of fine-grained soils with emphasis on toughness limit

It is well known that plasticity characteristics are one of the most important factors controlling the engineering behavior of fine-grained soils. In this regard, Atterberg limits are used for evaluation of plasticity and strength behavior of soils. Of all the plasticity identifiers, fall cone flow index, which is the slope of the flow curve is a descriptor of plasticity and undrained shear strength characteristics of cohesive soils. On the other hand, toughness limit is also efficient in prediction of many index properties of soils including plasticity characteristics, compression index as well as residual friction angle. This study is an attempt to establish relationships among fall cone flow index and consistency identifiers of fine-grained soils. Emphasis is given on toughness limit, which is believed to be a practical and efficient approach in assessment of fall cone flow index. Regression-based equations establishing relationships between fall cone flow index, plasticity index, plasticity ratio, toughness limit and liquid limit were presented. Besides, the efficiency of artificial neural networks in prediction of toughness limit and fall cone flow index was investigated. It should be noted that established relationships using regression equations are based on regional data, which come up with coefficient of determination (R2) values ranging between 0.706 and 0.978. On the other hand, ANN models were used to model global data, R2 values were obtained to be between 0.586-0.972 and 0.522-0.889 for training and testing phases, respectively. Relationships concerning local and global data were presented, along with analysis of effectiveness of application of relationships established for analysis of global data.

Fabrication of nitrogen-hyperdoped silicon by high-pressure gas immersion excimer laser doping

In recent years, research on hyperdoped semiconductors has accelerated, displaying dopant concentrations far exceeding solubility limits to surpass the limitations of conventionally doped materials. Nitrogen defects in silicon have been extensively investigated for their unique characteristics compared to other pnictogen dopants. However, previous practical investigations have encountered challenges in achieving high nitrogen defect concentrations due to the low solubility and diffusivity of nitrogen in silicon, and the necessary non-equilibrium techniques, such as ion implantation, resulting in crystal damage and amorphisation. In this study, we present a single-step technique called high-pressure gas immersion excimer laser doping (HP-GIELD) to manufacture nitrogen-hyperdoped silicon. Our approach offers ultrafast processing, scalability, high control, and reproducibility. Employing HP-GIELD, we achieved nitrogen concentrations exceeding 6 at% (3.01 × 1021 at/cm3) in intrinsic silicon. Notably, nitrogen concentration remained above the liquid solubility limit to ~1 µm in depth. HP-GIELD’s high-pressure environment effectively suppressed physical surface damage and the generation of silicon dangling bonds, while the well-known effects of pulsed laser annealing (PLA) preserved crystallinity. Additionally, we conducted a theoretical analysis of light-matter interactions and thermal effects governing nitrogen diffusion during HP-GIELD, which provided insights into the doping mechanism. Leveraging excimer lasers, our method is well-suited for integration into high-volume semiconductor manufacturing, particularly front-end-of-line processes.

Exploring the Effect of Aluminium Solute Atoms on Nanomechanical Properties of Copper‐Aluminium System using Nanoindentation

In this work, nanomechanical properties for Cu and Cu–Al (where Al = 2, 4, 6, and 8 wt%) alloys are determined using nanoindentation with Berkovich tip. Nanoindentation experiments are performed using trapezoidal load function where maximum load (Pm) varies from 1000 to 9000 μN at an equal interval of 1000 μN by maintaining equal loading and unloading rate. It is found that Cu8Al sample shows lower depth of penetration (h) at any load (P) in comparison to other samples due to more pinning of dislocations or hinderance of the dislocation movement by solute (Al) atoms during nanoindentation deformation. In addition, it is observed that the recovery is maximum for Cu8Al sample among all samples; however, this recovery is reducing with the increasing Pm for all samples. Thereafter, the elasticity and plasticity index are calculated from the area under the P–h curves, which shows that the elastic index is maximum for Cu8Al and minimum for Cu. Furthermore, the nanomechanical properties (reduced elastic modulus [Er] and hardness [H]) are reported for all samples and significant indentation size effect is observed for Cu8Al due to dislocation structure during deformation.

Determining the Suction Capacity of Compacted Clays with Fuzzy-Set Theory

Water suction capacity is an important parameter affecting the swelling properties and volumetric change of soil. Determination of the water suction capacity is made by the experiments which are needed long time in laboratory. However, this has random errors due to the heterogeneous and anisotropic structure of soil sample together with the error caused by the operator made the experiment. Such an estimation problem, included the errors, may be easily solved with the fuzzy-set theory. In this study, the suction capacity of compacted clayey soils is predicted with the fuzzy-set theory. For this reason, the engineering properties of clayey soil (plasticity index, dry density, initial water content and suction capacity) are partitioned into fuzzy subsets, and fuzzy rules are formed. Later, a computer program in the Fortran language is written to estimate the suction capacity of compacted clayey soil from these properties. It is shown that there is a good similarity between the results of the tests and proposed fuzzy logic model.

Enhancing marl soil stability: nanosilica’s role in mitigating ettringite formation

Marl soil is highly prone to erosion when exposed to water flow, posing a potential threat to structural stability. The common practice of stabilizing soil involves the addition of cement and lime. However, persistent reports of severe ruptures in many stabilized soils, even after extended periods, have raised concerns. In stabilized marls, unexpected ruptures primarily result from the formation of ettringite, which gradually damages the soil structure. This article aims to assess the impact of nanosilica on the formation of ettringite and the nanostructure of calcium silicate hydrate (C-S-H) during the marl soil stabilization process with lime. To achieve this, marl soil was stabilized with varying percentages of lime and nanosilica. X-ray diffraction (XRD) patterns and scanning electron microscopy (SEM) images were collected to observe changes in mineralogy and microstructural properties. Various geotechnical parameters, including granularity, Atterberg limits, compressive strength, and pH, were measured. The results indicate that the uniform distribution of nanosilica in marl-lime soils enhances pozzolanic activities, calcium aluminate hydrate growth (C-A-H), and the nanostructure of calcium silicate hydrate (C-S-H). According to XRD and SEM experiments, the presence of nanosilica reduces the formation of ettringite. Moreover, the compressive strength of modified samples exhibited an upward trend. In the experimental sample manipulated with 1% nanosilica combined with 6% lime, the compressive strength increased by 1.84 MPa during the initial 7 days, representing an approximately 18-fold improvement compared to the control sample.

Quantifying Desiccation Cracks for Expansive Soil Using Machine Learning Technique in Image Processing

The formation of desiccation cracks has detrimental effects on the hydraulic conductivity that affects the overall mechanical strength of expansive soil. Qualitative analysis on the desiccation cracking behaviour of expansive soil provided understanding of the subject based on various concepts and theories, while quantitative analysis aided these studies through numerical supports. In this study, a machine learning technique in image processing is developed to evaluate the surface crack ratio of expansive soil. The desiccation cracking tests were conducted on highly plastic kaolinite slurry samples with plasticity index of 29.1%. Slurry-saturated specimens with thickness of 10 mm were prepared. The specimens were subjected to cyclic drying-wetting conditions. The images are acquired through a digital camera (12 MP) at constant distance to monitor the desiccation cracks. The images are then pre-processed using OpenCV before crack feature extraction. In this study, a total of 54 desiccation crack images were processed, along with 8 images from trial test to train the model. The processed images are used to quantify the desiccation cracks by evaluating surface crack ratio and average crack width. It was identified that the accuracy of the model for the quantification of surface crack ratio and average crack width were 97.24% and 93.85% respectively with average processing time of 1.51s per image. The results show that the model was able to achieve high accuracy with sufficient efficiency in determining important parameters used for crack characterization.

Characterization of mine spoils for the reclamation of degraded lands of the Jos-Bukuru tin field, central Nigeria

Tin mining operations on the Jos Plateau have left a lasting impact on the environment, resulting in derelict land, mine spoils, and over a thousand abandoned mining ponds. These ponds have become potential hazards, endangering unsuspecting members of the public. This study aims to provide a comprehensive geochemical and geotechnical characterization of the mine spoils to assess their suitability for the reclamation and restoration of the degraded mined lands. The geochemical analysis reveals that the mine spoils exhibit a strong to moderate level of acidity, with a pH range of 3.91 to 5.90 (average: 4.85). Furthermore, they display deficiencies in essential nutrients such as phosphorus (average: 0.02%) and potassium (average: 0.31%), as well as trace elements such as copper (average: 19.15 ppm), manganese (average: 165.15 ppm), and zinc (average: 53.24 ppm). Notably, the mine spoils are enriched in iron (average: 6.31%). These findings highlight the inadequate presence of both micronutrient and macronutrient elements necessary for successful revegetation which is a crucial component of reclamation. Results from the geotechnical tests indicate that the mine spoils possess an average fines content of 56.03%, a mean liquid limit of 38.06%, and a mean plasticity index of 15.52%. Additionally, the average linear shrinkage value is measured at 10%. The average values for maximum dry density (MDD) and optimum moisture content (OMC) are 1.65 g/cm³ and 19%, respectively. The unsoaked California bearing ratio (CBR) averages 34.64%, while the coefficient of permeability for the spoil is approximately 1.24x10-3 mm/sec. Furthermore, the spoils average cohesion (C) was measured to be 12.150 while the mean angle of internal friction is 30.77 KN/M2. These results collectively indicate that mechanical and chemical stabilization, involving the use of lime, fly ash, and other soil-stabilizing agents is imperative for the effective reclamation of the degraded lands using the mine spoils. Such measures if adopted and implemented will mitigate the risk of accidents and drowning incidents from mining ponds and other abandoned pits involving unsuspecting members of the public and will contribute to the overall environmental restoration efforts in the Jos-Bukuru Tin Field.

Geotechnical and microstructural analysis of high-volume fly ash stabilized clayey soil and machine learning application

The weak soil stabilization using solid wastes is one of the most common solutions for improving geotechnical characteristics as well as for problematic waste dumping in landfills. The present experimental study aims to examine the effect of high-volume Class-F fly ash on the geotechnical and microstructural properties of clayey soil by adding them in ranges between 5 % and 50 %. The results show that as the amount of fly ash in clayey soil increases, properties like the specific gravity, plasticity index, permeability, optimum moisture content, maximum dry density and free swelling index improves. Moreover, these geotechnical properties were analyzed to develop machine learning models using three different algorithms, namely K-nearest neighbor regression, random forest, and support vector regression, for obtaining the optimum amount of fly ash contents in weak expansive soils. The predicted and experimental results found to be in close-relation for predicting the geotechnical behavior of modified clayey soil. Furthermore, the performance of the ML models degrades as the number of components reduces, with KNN regression consistently outperforming SVR and RF but suffering significantly with fewer components. The results of the testing set in the case of four components are MSE of 77, R² of 0.896, RMSE of 0.846, MAE of 0.327, and SEE of 0.858, indicating precise and consistent predictions. However, the prediction accuracy considering lesser components shows MSE as 262, R² as 0.648, MAE as 5.606, SEE as 16.707, and GPI as 1.056, confirming the elevated error rates. Overall, it has been concluded that combining comprehensive experimental work and machine learning techniques outperforms in enhancing geotechnical data processing, optimized waste contents in weak soils, improves sustainability in construction, saves resources, reduces the possibility of human mistakes, and increases reliability.

Mineralogical and Engineering Properties of Soils Derived from In Situ Weathering of Tuff in Central Java, Indonesia

This paper presents the results of borehole investigations and laboratory tests carried out to characterize the soils derived from in situ weathering of tuff in Central Java, Indonesia. The 70 m thick weathering profile of the Quaternary tuff consisted of residual soil and completely to highly decomposed rocks. The relatively low dry unit weight and cohesion but high water content, porosity, plastic and liquid limits, and angle of internal friction of the soils in the present study were related to the dominance of halloysite clay minerals. The established relationships to predict soil shear strength parameters from the soil plasticity index and standard penetration test (SPT) N-values were examined, and linear and non-linear relationships for soils derived from in situ weathering of tuff were proposed.

Comprehensive analysis of correlations between soil electrical resistivity and index geotechnical properties

This study investigates the relationships between soil electrical resistivity and key geotechnical properties to develop predictive models for designing grounding systems in electrical substations. Conducted on 30 soil samples from various different regions of Thailand, the research evaluates resistivity in relation to soil water content, plasticity index, and dry density. Using a comprehensive laboratory setup, the soil samples were evaluated under controlled conditions, including temperature, humidity, and salt content of the soil samples, to establish robust correlations between the measured resistivity and the index geotechnical parameters. The results reveal a significant inverse relationship between soil resistivity and water content, confirming that water content is a dominant factor influencing resistivity. Further analysis using multiple non-linear regression demonstrated strong correlations, indicating that a combination of water content, soil dry density, and plasticity index can predict soil resistivity with high reliability. This approach enables optimization of substation grounding designs to ensure operational safety and regulatory compliance. The study suggests that integrating these geotechnical assessments into standard grounding system design procedures can significantly improve the effectiveness and safety of electrical infrastructure. Recommendations for future research include expanding the dataset to encompass a broader range of soil types and investigating the effects of particle size and electrolyte content on soil resistivity. These enhancements aim to refine the predictive models and address the broader applicability of the results in diverse geological settings.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus.

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

Geographic variation in developmental plasticity among populations of the canyon treefrog in response to temperature and pond‐drying

AbstractUnderstanding how species respond to environmental changes, particularly in the context of climate change, is crucial for biodiversity conservation. This study focuses on the plastic responses of canyon tree frog (Dryophytes arenicolor) larvae to variations in temperature and pond‐drying, examining potential consequences of climate change. Frog larvae serve as an excellent model due to their high responsiveness to environmental cues during development. We analysed the impact of temperature and pond‐drying on morphological and life‐history traits, via a common garden experiment with individuals from three distinct populations with different ecological conditions. The experiments revealed significant differences in responses among populations, indicating geographic variation in plasticity. Pond‐drying treatments led to reduced survival and reduction of morphological traits and growth, challenging the assumption that tadpoles have adaptive responses to drying conditions. In contrast, temperature treatments showed variable effects, with elevated temperatures generally favouring growth rates, reducing metamorphosis time, and having population‐specific morphological shifts. We emphasize the importance of considering both morphological and life‐history traits, as well as geographic variation, in assessing species' vulnerability to climate change. Furthermore, the integration of environmental standardized plasticity index (ESPI) and relative distances plasticity index (RDPI) in amphibian developmental plasticity will allow to quantify and compare plastic responses among populations and even other amphibian species in which these metrics are obtained in the future. Our results underscore the complexity of phenotypic plasticity, revealing genotype–environment interactions. These findings contribute valuable insights into the potential adaptability of D. arenicolor populations to ongoing climate changes, highlighting the need for comprehensive inter‐population studies for a more nuanced understanding of species' responses to environmental change, and suggest that certain populations may be more vulnerable to extinction or better equipped to handle climate change based on their ability to adapt to environmental change.