Analysis of residual tropical cemented soil behaviour

In -situ soils in gee-material spectrum might arise due to sedimentation or could be non-sedimentary residual formations. The inherent nature and diversity of geological processes involved in the soil formation stage itself are responsible for a wide variability in the in-situ state of the soil. In this paper the possibility of analyses to arrive at engineering parameters of residual soils with varied degrees of residual or acquired cementation by the use of physical and in-situ parameters normally determined in routine investigations, are examined. An Intrinsic State Line,(ISL), with reference to an intrinsic state parameter (e/e(L)) and its variation with effective stress for reconstituted clays has been developed for residual tropical soils of non-sedimentary origin. In relation to the Intrinsic State Line (ISL), the undisturbed state, e, the potential parameter, e(L), along with the overburden pressure data has been analyzed to identify the dominance of cementation or stress history or both in controlling the compressibility and strength behaviour of natural residual soil. The location of yield stress point in relation to the ISL, pre-, and post- yield stress, compression indices along the e- log sigma(v) path provide a simple means to the analysis of the compressibility characteristics of cemented soils for analysis.

Mechanistic approach to the shear strength behaviour of allophanic soils

The presence of allophane minerals imparts special engineering features to the volcanic ash soils. This study examines the reasons for the allophanic soils exhibiting unusual shear strength properties in comparison to sedimentary clays. The theories of residual shear strength developed for natural soils and artificial soil mixtures and the unusual surface charge properties of the allophane particle are invoked to explain the high shear strength values of these residual soils. The lack of any reasonable correlation between phi' (effective stress-strength parameter) and plasticity index values for allophanic soils is explained on the basis of the unusual structure of the allophane particle. The reasons as to why natural soil slopes in allophanic soil areas (example, Dominica, West Indies) are stable at much steeper angles than natural slopes in sedimentary clay deposits (London clay areas) are explained in light of the hypothesis developed in this study.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Vermicompost reduces the invasiveness of the earthworm species Dichogaster bolaui compared to the use of compost in a degraded tropical soil in Northern Vietnam

In Northern Vietnam, organic fertilization programmes are being tested to restore soil fertility and reduce soil erosion. However, the amendment of organic matter in soil is also associated with the development of the invasive earthworm species Dichogaster bolaui. The objective of this study was to investigate the influence of organic matter amendment quality (compost vs. vermicompost) on D. bolaui. Our study confirmed D. bolaui development in organic patches in the field. However, we also observed that the flat-backed millipede Asiomorpha coarctata proliferated in these organic patches. Native to Asia, this millipede species is also considered as invasive in America. Both D. bolaui and A. coarctata more rapidly colonized compost than vermicompost patches. A laboratory experiment confirmed this trend and showed the limited development of D. bolaui in vermicompost. This is probably because of the decreased palatability of this substrate to soil fauna. In conclusion, any restoration practice that aims to increase the organic stocks in soils degraded by erosion should consider the quality of the organic amendment. In Northern Vietnam, vermicompost may be the preferred substrate for restoring soils while limiting the spread of D. bolaui. (C) 2014 Elsevier Masson SAS. All rights reserved.

Fire and soil temperatures during controlled burns in seasonally dry tropical forests of southern India

Fire and soil temperatures were measured during controlled burns conducted by the Forest Department at two seasonally dry tropical forest sites in southern India, and their relationships with fuel load, fuel moisture and weather variables assessed using stepwise regression. Fire temperatures at the ground level varied between 79 degrees C and 760 degrees C, with higher temperatures recorded at high fuel loads and ambient temperatures, whereas lower temperatures were recorded at high relative humidity. Fire temperatures did not vary with fuel moisture or wind speed. Soil temperatures varied between <79 degrees C and 302 degrees C and were positively correlated with ground-level fire temperatures. Results from the study imply that fuel loads in forested areas have to be reduced to ensure low intensity fires in the dry season. Low fire temperatures would ensure lower mortality of above-ground saplings and minimal damage to root stocks of tree species that would maintain the regenerative capacity of a tropical dry forest subject to dry season wildfires.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Use of Sr isotopes as a tool to decipher the soil weathering processes in a tropical river catchment, southwestern India

River water composition (major ion and Sr-87/Sr-86 ratio) was monitored on a monthly basis over a period of three years from a mountainous river (Nethravati River) of southwestern India. The total dissolved solid (TDS) concentration is relatively low (46 mg L-1) with silica being the dominant contributor. The basin is characterised by lower dissolved Sr concentration (avg. 150 nmol L-1), with radiogenic Sr-87/Sr-86 isotopic ratios (avg. 0.72041 at outlet). The composition of Sr and Sr-87/Sr-86 and their correlation with silicate derived cations in the river basin reveal that their dominant source is from the radiogenic silicate rock minerals. Their composition in the stream is controlled by a combination of physical and chemical weathering occurring in the basin. The molar ratio of SiO2/Ca and Sr-87/Sr-86 isotopic ratio show strong seasonal variation in the river water, i.e., low SiO2/Ca ratio with radiogenic isotopes during non-monsoon and higher SiO2/Ca with less radiogenic isotopes during monsoon season. Whereas, the seasonal variation of Rb/Sr ratio in the stream water is not significant suggesting that change in the mineral phase being involved in the weathering reaction could be unlikely for the observed molar SiO2/Ca and Sr-87/Sr-86 isotope variation in river water. Therefore, the shift in the stream water chemical composition could be attributed to contribution of ground water which is in contact with the bedrock (weathering front) during non-monsoon and weathering of secondary soil minerals in the regolith layer during monsoon. The secondary soil mineral weathering leads to limited silicate cation and enhanced silica fluxes in the Nethravati river basin. (C) 2015 Elsevier Ltd. All rights reserved.

Tree-ring based hydroclimate reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth century mega-drought and tropical Pacific influence

We present here the first statistically calibrated and verified tree-ring reconstruction of climate from continental Southeast Asia.The reconstructed variable is March-May (MAM) Palmer Drought Severity Index (PDSI) based on ring widths from 22 trees (42 radial cores) of rare and long-lived conifer, Fokienia hodginsii (Po Mu as locally called) from northern Vietnam. This is the first published tree ring chronology from Vietnam as well as the first for this species. Spanning 535 years, this is the longest cross-dated tree-ring series yet produced from continental Southeast Asia. Response analysis revealed that the annual growth of Fokienia at this site was mostly governed by soil moisture in the pre-monsoon season. The reconstruction passed the calibration-verification tests commonly used in dendroclimatology, and revealed two prominent periods of drought in the mid-eighteenth and late-nineteenth enturies. The former lasted nearly 30 years and was concurrent with a similar drought over northwestern Thailand inferred from teak rings, suggesting a ``mega-drought'' extending across Indochina in the eighteenth century. Both of our reconstructed droughts are consistent with the periods of warm sea surface temperature (SST)anomalies in the tropical Pacific. Spatial correlation analyses with global SST indicated that ENSO-like anomalies might play a role in modulating droughts over the region, with El Nio (warm) phases resulting in reduced rainfall. However, significant correlation was also seen with SST over the Indian Ocean and the north Pacific,suggesting that ENSO is not the only factor affecting the climate of the area. Spectral analyses revealed significant peaks in the range of 53.9-78.8 years as well as in the ENSO-variability range of 2.0 to 3.2 years.

Late Quaternary vegetational and climatic changes from tropical peats in southern India - An extended record up to 40,000 years BP

Stable carbon isotope ratios of peats dated (by C-14) back to 40 kyr BP from the montane region (> 1800 m asl) of the Nilgiris, southern India, reflect changes in the relative proportions of C3 and C4 plant types, which are influenced by soil moisture (and hence monsoonal precipitation), From prior to 40 kyr BP until 28 kyr BP, a general decline in delta(13)C values from about - 14 per mil to - 19 per mil suggests increased dominance of C3 plants concurrent with increasingly moist conditions, During 28-18 kyr BP there seems relatively little change with delta(13) C of - 19 to - 18 per mil, At about 16 kyr BP a sharp reversal in delta(13)C to a peak of - 14.7 per mil indicates a clear predominance of C4 vegetation associated with arid conditions, possibly during or just after the Last Glacial Maximum, A moist phase at about 9 kyr BP (the Holocene Optimum) with dominance of C3 vegetation type is observed, while arid conditions are re-established during 5-2 kyr BP with an overall dominance of C4 vegetation, New data do not support the occurrence of a moist phase coinciding with the Mediaeval Warm Period (at 0.6 kyr BP) as suggested earlier, Overall, the climate and vegetation in the high altitude regions of the southern Indian tropics seem to have responded to past global climatic changes, and this is consistent with other evidences from India and other tropical regions.

Reproductive phenology of a tropical dry forest in Mudumalai, Southern India

1 Flowering and fruiting phenologies of a tropical dry forest in Mudumalai, southern India, were studied between April 1988 and August 1990. Two sites, a wetter site I receiving 1100mm and a drier site II receiving 600mm of rainfall annually, are compared. A total of 286 trees from 38 species at site I and 167 trees from 27 species at site II was marked for phenological observations. There were 11 species common to the two sites. Several hypotheses relating to the evolution of reproductive phenology are tested. 2 Frequency of species flowering attained a peak at site I during the dry season but at site II, where soil moisture may be limiting during the dry months, the peak was during the wet season. At both sites a majority of species flushed leaves and flowered simultaneously. Among various guilds, the bird-pollinated guild showed distinct dry season flowering, which may be related to better advertisement of large flowers to pollinators during the leafless dry phase. The wind-pollinated guild flowered mainly during the wet season, when wind speeds are highest and favourable for pollen transport. The insect-pollinated guild showed no seasonality in flowering in site I but a wet season flowering in site II. 3 Fruiting frequency attained a peak in site I during the late wet season extending into the early dry season; a time-lag correlation showed that fruiting followed rainfall with a lag of about two months. Site II showed a similar fruiting pattern but this was not statistically significant. The dispersal guilds (animal, wind, and explosive passively-dispersed) did not show any clear seasonality in fruiting, except for the animal-dispersed guild which showed wet season fruiting in site I. 4 Hurlbert's overlap index was also calculated in order to look at synchrony in flowering and fruiting irrespective of climatic (dry and wet month) seasonality. In general, overlap in flowering and fruiting guilds was high because of seasonal aggregation. Among the exceptions, at site II the wind-pollinated flowering guild did not show significant overlap between species although flowering aggregated in the wet season. This could be due to the need to avoid heterospecific pollen transfer. 5 Rarer species tended to flower earlier in the dry season and this again could be an adaptation to avoid the risk of heterospecific pollen transfer or competition for pollinators. The more abundant species flowered mainly during the wet season. Species which flower earlier have larger flowers and, having invested more energy in flowers, also have shorter flower to fruit durations.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.