Showing papers in "Transportation geotechnics in 2021"
TL;DR: In this article, gene expression programming (GEP) and multi-expression programming (MEP) are utilized to formulate new prediction models for determining the compaction parameters (ρdmax and wopt) of expansive soils.
Abstract: In this study, gene expression programming (GEP) and multi gene expression programming (MEP) are utilized to formulate new prediction models for determining the compaction parameters (ρdmax and wopt) of expansive soils. A total of 195 datasets with five input parameters (i.e., clay fraction CF, plastic limit wP, plasticity index IP, specific gravity Gs, maximum dry density ρdmax), and two output variables ρdmax and wopt are collected from the literature comprising 119 internationally published research articles to develop the GEP and MEP models. Simplified mathematical expressions were derived for these models to determine the ρdmax and wopt of expansive soils. The performance of the models was tested using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). Sensitivity and parametric analyses were also performed on the GEP and MEP models. Additionally, external validation of the models was also verified using commonly recognized statistical criteria. It is clear from the results that the GEP and MEP methods accurately characterize the compaction characteristics of expansive soils resulting in reasonable prediction performance, however, GEP model yielded relatively better performance. Also, the proposed predictive models were compared with previously available empirical models and they exhibited robust and superior performance. Moreover, the ρdmax model provided significantly improved results as compared to the wopt prediction model in the case of GEP, and vice versa in the MEP model. It is therefore recommended that the proposed GP based models can reliably be used for determining the compaction parameters of expansive soils which effectively reduces the time-consuming and laborious testing, hence attaining sustainability in the field of geo-environmental engineering.
68 citations
TL;DR: The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than ±20% deviation from the experimental data for 97.27% of the samples.
Abstract: This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than ±20% deviation from the experimental data for 9727% of the samples The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 1240–7597 and which is made available as supplementary material
66 citations
TL;DR: Enhanced Adaboost models were used to classify soil types base on tree algorithm models that are less commonly used in this area to increase the accuracy and reduce the cost of projects.
Abstract: This research focuses on presenting new models based on classifiers that can be applied to various problems. Adaboost is a type of ensemble learning machine that uses classifiers that contain a range of base models. This study used enhanced Adaboost models to classify soil types base on tree algorithm models that are less commonly used in this area. Determining the type of soil in different geotechnical projects is very important. Using soil classification, soil properties such as mechanical properties, performance against static and dynamic loads can be found. Regarding the importance of the subject, 440 samples of the actual project were used to design this new methodology. The dataset included clay content, moisture content, specific gravity, void ratio, plastic, and liquid limit parameters to determine the type of soil classification. These samples were tested with high precision and the actual type of classification was obtained. For comparison, two enhanced tree and neural network model were designed and developed according to these conditions. The results of this classification were presented for different soil samples. The developed adaboost model showed that it could well classify the soil. This model showed that only 11 samples were not correctly identified among the total data (88 data). Therefore, this new technique can be used to increase the accuracy and reduce the cost of projects.
61 citations
TL;DR: In this paper, three non-destructive tests, namely Schmidt hammer, p-wave velocity, and density, were performed on 127 granitic rock samples, and their results were considered as input parameters.
Abstract: Tensile strength of rock plays a significant role in the design of tunnels and underground engineering projects. Due to the inefficiency of direct method in determining rock tensile strength, the use of non-destructive tests has become a new direction in predicting the Brazilian Tensile Strength (BTS) of the rock samples. Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are three widely-used methods for BTS prediction. This study investigated the performance of these three intelligent models for BTS forecasting. In this regard, three non-destructive tests, namely Schmidt hammer, p-wave velocity, and density, were performed on 127 granitic rock samples, and their results were considered as input parameters. Then, the BTS tests were carried out on the samples and their results were considered as model output. Four measures of coefficient of determination (R2), Root mean square error (RMSE), Mean absolute error (MAE), and Scatter index (SI) were used for evaluation. The results showed that the ANFIS model, which is enjoying advantages of both ANN and FIS models, provides more accurate results in comparison with the proposed ANN and FIS models in predicting BTS values. R2 values for ANFIS, ANN, and FIS models were 0.92, 0.88, and 0.87, respectively. Besides, the ANFIS model could yield the lowest RMSE value of 81.5%, whereas RMSEs for FIS and ANN were 89.5% and 87.5%, respectively.
59 citations
TL;DR: This paper shows the workability of two soft computing techniques in predicting the deformation of GRS structures and recommends that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures.
Abstract: The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to show the workability of two soft computing techniques in predicting the deformation of GRS structures. For this reason, first a relevant case study was modelled into ABAQUS, a finite element (FE) software. Then, the FE results (GRS deformations) were checked against the recorded deformations of the full-scale test. Subsequently, 166 finite element analyses were performed for dataset construction. Then, two predictive models of GRS deformations were constructed. For intelligent model construction, two artificial neural networks (ANN) were coupled with Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), respectively. It was found that both GSA-based ANN and PSO-based ANN predictive models work good enough. However, the correlation coefficient (R) of 0.981 as well as the system error of 0.0101 for testing data suggest that the GSA-based ANN predictive model outperforms the PSO-based ANN model with R value of 0.973 and system error of 0.0127. Overall, findings recommend that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures.
57 citations
TL;DR: In this article, bottom ash (BA) was used to chemically treat the expansive soil and coir fibers (CF) as reinforcement against tensile cracking to stabilize the soil subgrade.
Abstract: This study explored the coupling effect of the recycled ash and natural fibers to control the expansive soil's strength and durability attributes The bottom ash (BA) was used to chemically treat the expansive soil and coir fibers (CF) as reinforcement against tensile cracking The sustainable use of BA and CF to stabilize the expansive soil has been demonstrated by assessing - swelling behavior, mechanical and chemical properties The expansive soil was stabilized with 5%, 10%, 15% and 20% BA and reinforced with 025%, 050% and 100% CF The curing period of 28 days was considered for the stabilization of the soil This study presents individual material's effect to stabilize the expansive soil subgrade and also the coupling effect of both fibers and ash The durability of stabilized expansive soil has been assessed by investigating the mechanical and chemical properties before and after 2nd, 4th, 6th, 8th and 10th freeze–thaw cycles The BA stabilized expansive soil exponentially reduces the upward swelling pressure and controls the plasticity behavior An increase in the percentage of BA has increased the calcite content, pH, and electrical conductivity The unconfined compressive strength and split tensile strength have been increased due to BA and CF The CF reinforced specimens shows less loss in mechanical strength during freeze–thaw cycles and gives higher tensile strength The effective mechanism of BA and CF stabilized expansive soil is discussed in detail The BA and CF can be effectively used to stabilize the expansive soil for the application of road pavements The approach used here to stabilize pavement subgrades is sustainable and will provide economical solutions
46 citations
TL;DR: In this paper, the feasibility of using volcanic ash (VA)-based geopolymer as an alternative soil stabilizer to cement by comparing their shear strength behavior and life cycle assessment was investigated.
Abstract: There is a growing interest in developing environmentally-friendly substitution for Portland cement in soil stabilization. This study evaluated the feasibility of using volcanic ash (VA)-based geopolymer as an alternative soil stabilizer to cement by comparing their shear strength behavior and life cycle assessment (LCA). The effects of curing conditions, vertical confinements, binder contents, and alkali activator properties were investigated. The results revealed that regardless of the type of binder, increasing binder content changes the structure of clayey soil through aggregation, thus improves the shear resistance. The interparticle bonds developed faster at higher curing temperatures, and the interlocking of the particles increased at higher confining pressures. Based on the determined boundary conditions, the LCA suggested a comparative environmental impact for both binders to stabilize 1 m3 functional unit of clayey soil with similar shear strength.
44 citations
TL;DR: Evaluating the performance of the long short term memory (LSTM), deep neural networks (DNN), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision tree (DT) to predict the UCS proved that computational intelligence approaches are capable of predicting UCS.
Abstract: The uniaxial compressive strength (UCS) is a vital rock geomechanical parameter widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. The main objective of the present study is to evaluate the performance of the long short term memory (LSTM), deep neural networks (DNN), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision tree (DT) to predict the UCS of different rock types of Claystone, Granite, Schist and Sandstone, Travertine, Limestone, Slate, Dolomite and Marl acquired from almost all quarry locations of Iran. 170 data sets, including porosity (n), Schmidt hammer (SH), P-wave velocity (Vp), and point load index (Is(50)) were applied in the methods. Finally, a comparison was made between the results made by the prediction methods. To assess the performance ability of the applied methods, the 5-fold cross-validation (CV) was considered. The results proved that computational intelligence approaches are capable of predicting UCS. On the whole, the GPR with a correlation coefficient (R2) of 0.9955 and a route mean square error (RMSE) of 0.52169, performs best. Lastly, the UCS prediction intelligence methods were ordered as GPR, DT, SVR, LSTM, DNN and KNN, respectively.
42 citations
TL;DR: In this paper, the authors studied the soil's blowout stability above a damaged water main pipeline in three idealized stages of internal soil erosion, i.e., horizontal, semi-circular, and circular cavities using the latest finite element limit analysis technique.
Abstract: Sinkhole incidents have increased rapidly in recent decades due to water main breaks. Although numerous researchers have recently conducted investigations in sinkhole phenomenon, most of the studies are related to natural sinkhole formation, underground cavity detection, and collapse analysis. Very few studies can be found in relation to the blowout stability of soils due to defective pipeline under high water main pressures, in spite the frequent media news about the water main bursts which enlighten the relevance of the problem. The present paper aims to study the soil's blowout stability above a damaged water main pipeline in three idealized stages of internal soil erosion, i.e. horizontal, semi-circular, and circular cavities using the latest finite element limit analysis technique. Dimensionless design parameters are used throughout the paper to present rigorous bounding solutions that can be used directly by designers to evaluate blowout stability of soils above defective pipelines. Design charts and tables are presented to cover a wide range of design parameters, and a practical example is introduced to illustrate their use in practice.
37 citations
TL;DR: In this paper, the effects of recycled tire polymer fibers (RTPF) and glass fibers (GF) on enhancing the strength/deformation properties of clays were investigated in a series of compaction, unconfined compression and direct shear tests.
Abstract: Soil reinforcement with fibers is a simple, efficient and low-cost (especially by incorporating waste or recycled fibers) technique for enhancing mechanical characteristics of soft soils. This experimental study investigates the effects of recycled tire polymer fibers (RTPF) and glass fibers (GF) on enhancing the strength/deformation properties of clays. A series of compaction, unconfined compression and direct shear tests were performed on precisely prepared composite soils comprising clay, with different amounts (i.e. 0.5, 1.0 and 1.5%) of RTPF and GF having varying lengths (5 and 10 mm). Laboratory findings indicated that the composite soils have lower dry density and higher optimum moisture content as compared to the clay. Both unconfined compression and shear strength test results on composite soils implied that while adding fibers increased the materials’ ductility, there was an optimal percentage of fibers (0.5% for RTPF and 1.0% for GF) causing the highest strength gain, beyond which strength decreased. Concerning the shear strength parameters, i.e. cohesion intercept and internal friction angle, the fiber inclusion was particularly influential in increasing cohesion, whereas changes in internal friction angle were minimal. Moreover, the shorter fibers (5 mm) were generally more efficient in reinforcing the clay. The fiber improved composite soils have a better ductility and load-bearing capacity compared to parent soils that can be beneficial in the construction of pavement and railway substructure, liners, small fills/dikes, backfills around pipes, and slope protections.
33 citations
TL;DR: In this article, ground, flaky, and pelleted shapes of four sorted types of plastic waste from a recycling market were combined with silty or clayey gravel and sand soil of the A-2-7 AASHTO type.
Abstract: Scarcity of traditional construction materials has motivated researchers to explore alternatives, and besides crushed glass, reclaimed asphalt pavement, and scrap tires, to name a few, plastic waste (unwanted or unusable plastic objects) has also gained attention in recent years. Plastic waste is traditionally re-used or recycled, but it often ends up as trash on curbsides, in landfills, or in our seas and oceans. The substantial amount of plastic waste produced annually worldwide, and its environmental repercussions are the rationale for exploring alternatives in order to recycle plastic waste into construction materials. This exploration can also benefit many industries and would help minimising adverse environmental impacts associated with dumping tones of plastic waste in landfills. Using plastic waste material with soil for soil reinforcement purposes has revealed some improvements in terms of strengths of materials, but nevertheless, this potentiality has not been fully assessed for different types and forms of plastic waste with natural subgrade soil in the road industry. In this paper, ground, flaky, and pelleted shapes of four sorted types of plastic waste from a recycling market were combined with silty or clayey gravel and sand soil of the A-2-7 AASHTO type. These plastic types are: low density polyethylene (LDPE), high density polyethylene (HDPE), polyethylene terephthalate (PETE), and polypropylene (PP) resins. Their various geotechnical properties have been assessed thoroughly. The investigation process entails assessing compaction, Californian Bearing Ratio (CBR), strength, resilient modulus, and permeability properties for both natural sub-grade soil and modified sub-grade soil with the aforementioned types of plastic waste. The results obtained show that the addition of plastic wastes decrease the maximum dry densities of the subgrade soils because of the lower relative density of the plastic material compared to the soil particles. It is also found that the addition of plastic wastes can increase or decrease the CBR and MR values of the subgrade. The nature of change (increase or decrease) and its magnitude are a function of the plastic content, shape and type. Permeability values of many subgrade soil samples increased with the addition of plastic waste, whereas the hydraulic conductivity of some soils modified with plastic remained unchanged. Subgrade soils with plastic had higher friction angle and lower compressive strength than plastic-free soils. The results of this research suggest that partial replacement of subgrade soil material with plastic waste may prove useful in road subgrade applications.
TL;DR: An overview of bio-based soil stabilisation techniques can be found in this paper, where the primary challenges that lay ahead for future research in Bio-based stabilisation products application in the road sector and the innovations to address the challenges of using modernised techniques in road construction industry (i.e., weak subgrade and the required maintenance thereof).
Abstract: In situ soil modification is required in order to improve the primary engineering properties of the material to meet a road construction standard. Bio-stabilised soil is an environmentally friendly, cost-effective alternative to imported granular fills, concrete, costly hauling of materials or export to a landfill. In-service soil performance and required maintenance is highly dependent on methods of stabilisation, ranging from expensive mechanical stabilisation to chemical processes. As such, many alternative materials originating from bio-based sources are being explored as potential stabilising additives to improve weak subgrade soils (i.e., dispersive, erodible and collapsible soil, and soft or expansive clays). Some key solutions include the use of bio-derived enzymes, microbes, and polymeric additives to avert road failure caused by water penetration and/or erosion. The role of microbial substrate specialisation has been largely unexplored, since the level of research done on alternative stabilisers consists mostly of small ad hoc studies. In addition, research has focused on a reduction in permeability and an increase in compressive strength using enzymes and polymers, however, the complexity of these products and their implementation for a wide range of soil types and structural applications remain limited. Currently there is a need for more supporting research methodologies and systematic approaches on the implementation of bio-based materials for infrastructure development. This also includes the simplification of bio-based products for potential construction applications. This review provides (a) an overview of soil stabilisation techniques, (b) the primary challenges that lay ahead for future research in bio-based stabilisation products application in the road sector and (c) innovations to address the challenges of using modernised techniques in the road construction industry (i.e., weak subgrade and the required maintenance thereof, as well as the development of potential bio-based additives for unpaved road construction application).
TL;DR: In this paper, a field measurement of the train-induced vibration in the northwest Shanghai, where Shanghai Metro Line 11 crosses under Beijing-Shanghai Railway is presented, where ground and tunnel accelerations are measured for the separate and simultaneous passages of the ground and subway trains.
Abstract: This paper presents a field measurement of the train-induced vibration in the northwest Shanghai, where Shanghai Metro Line 11 crosses under Beijing-Shanghai Railway. Ground and tunnel accelerations are measured for the separate and simultaneous passages of the ground and subway trains. Vibration characteristics are subsequently analyzed in the time domain and in the frequency domain. Measurement results show that the ground vibration acceleration level (VAL) induced by the simultaneous operation of the ground and subway trains is 6 ~ 13 dB greater than that induced by the separate operation for the frequency range 20 ~ 160 Hz. Significant frequency components of the vibration induced by the simultaneous operation are shown to be redistributed to cover those induced by the separate operation. Measured data in this experimental test can also be used to validate future numerical models for predicting train induced vibration of similar embankment-tunnel systems.
TL;DR: The findings show that the relationship between the PCI and IRI can vary significantly based on factors such as location, functional class and slope.
Abstract: Two of the most popular pavement performance indicators are the International Roughness Index (IRI) and the Pavement Condition Index (PCI). The Long-Term Pavement Performance (LTPP) database does not include the latter. Therefore, limited research is available on the relationship between the PCI and IRI based on the LTPP roads. This study aims to cast light on the relationship between these two performance indicators using LTPP data. To this end, 3,954 records of IRI and PCI were collated to determine the correlation. The aggregate goodness of fit was not satisfactory (R2 = 0.31) as the data was collected over 61 different states and provinces and in a 28-years timeline. So, in the next step the data was clustered into more meaningful groups based on location (province/state) and functional class in the hope of improving the goodness of fit. It was observed that the R2 within each group was substantially higher than the aggregate data, with some reaching above 0.70. Preparing an unprecedentedly large dataset gave us the freedom of segmenting the data into smaller and less noisy subsets, which can result in more robust models with higher coefficients of determination. Moreover, another dataset collected by Ontario Ministry of Transportation (MTO) was studied and the results were contrasted against each other. It was observed that the MTO data is more cohesive, and the correlation between the IRI and the PCI was stronger in that dataset. Finally, this study investigated the variations not explained by regression models, i.e. reasons that road sections can have an excellent PCI and poor IRI and vice versa. The findings show that the relationship between the PCI and IRI can vary significantly based on factors such as location, functional class and slope.
TL;DR: Results show that developed models have a great ability to mimic the nonlinear relationships between UCS and its influential variables and PSO-ANN presents the best performance among three models on the training dataset.
Abstract: The use of cement as a curing agent has been widely adopted in soft soil engineering to increase the strength of soft soil. The cemented soil is gradually exposed to the air and in a natural environment becomes unsaturated. Unconfined compressive strength (UCS) of the unsaturated cemented soils is a key parameter for assessing their strength behaviour. UCS determination of unsaturated cemented soils by using laboratory methods is a complex, time-consuming, and expensive procedure due to the difficulty in suction control. This study aims to model the UCS of unsaturated cemented Wenzhou clay, i.e., capture the nonlinear relations between UCS and its influential variables including cement content (%), dry density (g/cm3) and suction (MPa) for the first time by using machine learning approach. Toward this aim, three advanced computational frameworks are developed based on hybrid evolutionary approaches in which evolutionary optimisation algorithms including genetic algorithm (GA), particle swarm optimisation (PSO) and imperialist competitive algorithm (ICA) are hybridised with artificial neural network (ANN). Results show that developed models have a great ability to mimic the nonlinear relationships between UCS and its influential variables and PSO-ANN presents the best performance among three models on the training dataset with R 2 = 0.9888 , RMSE = 0.129 and VAF = 97.742 , and testing dataset with R 2 = 0.9412 , RMSE = 0.237 and VAF = 90.414 . To facilitate engineering application, an engineering database for Wenzhou soft clay at different cement ratios (up to 11%), suctions (up to 300 MPa) and dry densities (1–1.5 g/cm3) is built by using the developed PSO-ANN model.
TL;DR: In this paper, the efficiency of fly ash based soil stabilization can be improved using secondary additives, such as lime, CSA cement, enzyme and polymers were utilized as secondary additives.
Abstract: Expansive soils are widespread in many parts of the world. Due to its low strength, high compressibility, and massive volumetric changes, these soils are a potential origin of damage to roads, buildings, foundations and other geo-infrastructure. Extensive research has been conducted on the utilisation of fly ash to stabilize expansive soils. This paper describes how the efficiency of fly ash based soil stabilization can be improved using secondary additives. Class F fly ash, an industrial by-product, was used as the base additive. Lime, CSA cement, enzyme and polymers were utilized as secondary additives. A series of mechanical and microscopic tests (CBR, compaction test, SEM, XRD, FTIR and TGA) was carried out on different combinations of additives. The results indicate that secondary additives can be effectively used to improve the efficiency of fly ash based soil stabilization. Soil-fly ash-lime-enzyme was identified as an optimum combination to enhance bearing capacity while soil-fly ash-lime and soil-fly ash-enzyme also showed substantial improvements in subgrade performance. Findings from laboratory investigations were verified applying into 3-D numerical modelling to evaluate the pavement performance which revealed substantial benefits of pavement thickness reduction when fly ash stablized weak soils are treated using secondary additives.
TL;DR: In this article, five numerical tests of seepage erosion in granular soils around the tunnel were conducted using the Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) coupling method.
Abstract: For tunnels built in the saturated silty sand ground, fine particles may be migrated into tunnels through seams of tunnel segmental joints and then seepage erosion is triggered, which may induce ground settlement. However, the process from fine particles erosion to the stress redistribution and soil properties’ change surrounding the tunnel and ground settlement has not been clarified up to now. For this purpose, five numerical tests of seepage erosion in granular soils around the tunnel are conducted using the Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) coupling method. The influences of buried depth and grain size distribution (GSD) of gap graded soils (mainly controlled by the fines content and mean particle size ratio from coarse to fine) on the seepage erosion around the tunnel are investigated. Eroded mass, fines loss mode, surface vertical displacement, stress redistribution, fabric anisotropy, soil behavior and water pressure around the tunnel during the seepage erosion process for five tests are presented and compared. The following results can be upscaled to the practical tunnel engineering, such as: (1) the number of fines loss, the eroded zone and the ground settlement increase with buried depth and mean particle size ratio; (2) the earth pressure near the crack significantly increases due to the stress redistribution induced by fines loss, and the stress redistributed area expands with buried depth; (3) the strength and stiffness of granular soils around the crack are significantly reduced by the seepage erosion. All results revealed that the CFD-DEM simulations provide a new sight on understanding the mechanics of tunnel seepage erosion from a microscopic perspective.
TL;DR: It is expected that the appropriate use of geogrids can be a significant cost saving per project and applying GE factors to a pavement design to reduce the thickness of gravel and/or asphalt and consequently extend the service life and reduce maintenance costs.
Abstract: Many researchers have conducted laboratory studies for assessing the interaction mechanism of soil and geosynthetics and have shown that the performance of flexible pavement is enhanced by geosynthetic reinforcement through extending their service life or decreasing the base course thickness. However, there is a lack of comprehensive comparisons between different studies. This paper reviews laboratory studies available in the literature and presents a review of the main contributions. This literature review reveals that improvement of the performance due to the geosynthetic reinforcement relates to various factors and variables, including geogrid stiffness and geometry, geogrid location/depth, asphalt surface and aggregate thicknesses, and subgrade stiffness. Based on synthesizing laboratory testing studies, a regression-based formulation is proposed to predict the Granular Equivalent (GE) factor of geogrid reinforcement of flexible pavements. The predictive model is robust as it includes the key parameters mentioned above. This formula was developed from a regression analysis by back calculating the variety of the results of the performed experimental tests using the AASHTO1993 design method to evaluate the equivalent base course thickness of reinforced sections compared to unreinforced sections. The benefit of this study is realizing and understanding the structural benefits of geogrids and applying GE factors to a pavement design to reduce the thickness of gravel and/or asphalt and consequently extend the service life and reduce maintenance costs. It is expected that the appropriate use of geogrids can be a significant cost saving per project.
TL;DR: In this paper, the authors investigated the cyclic/dynamic loading behavior of geopolymer-treated kaolin clay using triaxial tests performed under stress-controlled conditions and range of amplitudes and frequencies.
Abstract: In recent years, fly-ash based geopolymers have been an alternate binder to ordinary Portland cement commonly used for soil stabilization, as they were found to enhance soil strength and stiffness with the benefits of entailing lower toxic pollution and energy usage. However, most available studies on geopolymer-stabilised soils have focussed on the mechanical performance of treated soils under static loading rather than cyclic/dynamic loading, which requires further research. This paper investigates the cyclic/dynamic loading behaviour of geopolymer-treated kaolin clay using triaxial tests performed under stress-controlled conditions and range of amplitudes and frequencies. The results indicate that the inclusion of geopolymer considerably enhances the cyclic performance of treated clay in terms of soil attainable accumulated strain, number of load cycles and cyclic shear modulus. Although it is found that a small amount of geopolymer can enhance the initial cyclic response, a larger quantity of geopolymer is necessary to maintain sufficient durability for treated clay over successive loading cycles. However, the enhanced cyclic response of treated clay appeared to be influenced by the increase in stress intensity and applied frequency. It is concluded from this study that geopolymer-treated clay may be suitable to support cyclic loading systems subjected to low loading amplitudes and frequencies such as roads and railways.
TL;DR: In this paper, the bearing characteristics of different types of cast-in-place piles, with or without grouting, based on a bridge construction in Wuqi-Dingbian Expressway, were investigated, and an in-situ static load test was carried out on each type of hole-forming method.
Abstract: This paper studies the bearing characteristics of different types of cast-in-place piles, with or without grouting, based on a bridge construction in Wuqi-Dingbian Expressway. Manual digging piles (MDPs), rotary drilling piles (RDPs), and impact drilling piles (IDPs) are investigated, and an in-situ static load test is carried out on each type of hole-forming method. The post-grouting results show that the grouting quantity of RDP is the largest, which is 3.15 t, and the IDP is the smallest, which is 1.5 t. According to the static load test, the ultimate bearing capacity of IDP is the largest and increases the most after grouting, while MDP has the smallest increase in ultimate bearing capacity. The IDP also displays the most significant increase of pile side resistance and largest proportion of pile end resistance to the total load, showing obvious characteristics of the end bearing friction pile. The calculation method of ultimate bearing capacity of the post-grouting pile recommended by Chinese code is also verified in this work, illustrating that it is necessary to study the upper return height of slurry in post-grouting. The obtained research results provide an increased understanding of the post-grouting mechanism and reference for the rational calculation of the bearing capacity of post-grouting pile and hole-forming method selection.
TL;DR: In this article, a review highlights several beneficial mechanical and physical characteristics of the Hydrophobic PU foam, which may nominate this material to be viable alternative stabilizers for the problematic expansive soil.
Abstract: Despite the variety of available stabilization methods for the expansive soil, a rapid stabilization and remediation solution for this class of the problematic soil is still needed. Among the available treatment methods, traditional chemical additives such as lime, and cement exhibit satisfying performance over their counterparts. Nevertheless, significant concerns are associated with those chemicals, such as sulfate, carbonation attack, and its environmental impact. The efficiency of the polyurethane (PU) foam as a stabilizing agent for the pavement subbase layer and foundation systems concerned with relative movement and excessive settlement problems has been confirmed in many experimental and in-situ studies. However, only a few studies preliminary explored the effect of this injected stabilizer on the response of swelling soil. This review highlights several beneficial mechanical and physical characteristics of the Hydrophobic PU foam, which may nominate this material to be viable alternative stabilizers for the problematic expansive soil. The paper also identified future research needs to fulfill the scientific and practical gap in using PU foam for expansive soil treatment.
TL;DR: In this paper, the authors evaluate the deterioration conditions of flexible pavements from their deflection profiles under the moving loads using artificial intelligence (AI)-based finite element (FE) model updating.
Abstract: With the development and application of highway-speed nondestructive testing (NDT) devices, a reliable method to evaluate the current level of deterioration of pavements from the measured results is necessary to provide comprehensive and timely maintenance and rehabilitation decisions for pavements. The objective of this study is to evaluate the deterioration conditions of flexible pavements from their deflection profiles under the moving loads. The deterioration is due primarily to the development and growth of microcracks in flexible pavement materials and is commonly called the “crack initiation” phase. This phase is characterized and represented by the decrease of the dynamic modulus and the increase of the phase angle. To simulate a flexible pavement under the moving loads, a three-dimensional (3D) finite element (FE) model is constructed using a commercial FE software and an equivalent two-dimensional (2D) axisymmetric FE model is built using the artificial intelligence (AI)-based FE model updating. The deflection profiles of the pavement model are analyzed and the time lag between the load and deflection peaks is used to define a new term named “lag angle” to represent the structural response of the flexible pavement under the moving loads. It is also found the lag angle is closely related to the degree of deterioration of the pavement, the speed of the moving load, the structural and material properties of the pavement, which reveals a promising application of the lag angle in the evaluation of flexible pavements using highway-speed NDT devices.
TL;DR: In this paper, the effect of influence factors including soil type (low and high fines content), NRL type, replacement ratio and cement content on the compressive strength prior to wetting and drying test (UCS0), cyclic wet-and-drying Compressive Strength (W-d)) and weight loss was examined in a study.
Abstract: Cement stabilized soil as pavement base and sub-base materials has been extensively applied in various countries. Nevertheless, cement stabilized soil in tropical countries undergoes cracking problems and premature pavement distress, due to cyclic wet and dry seasons. Natural rubber latex (NRL) can be used as an additive to improve the serviceability and durability of cement stabilized soil. The effect of influence factors including soil type (low and high fines content), NRL type (low to high dry rubber content), NRL replacement ratio and cement content on the compressive strength prior to wetting and drying test (UCS0), cyclic wetting and drying compressive strength (UCS(w-d)) and weight loss was examined in this study. The cement-NRL stabilized samples had higher UCS values than the cement stabilized samples, for all cement contents and NRL replacement ratios tested. The NRL films enhanced the cohesion (inter-particle forces) but retarded the hydration effects. The highest UCS value was found at an optimum NRL replacement ratio, which was 20%, 15%, and 10% for 3%, 5%, and 7% cement, respectively. The lowest weight loss and highest UCS(w-d) were also found at the optimum NRL replacement ratio. The UCS(w-d) of cement-NRL stabilized sample was found to be higher than that of cement stabilized samples at all w-d cycles, even for the same UCS0. The equation of predicting UCS(w-d) at various w-d cycles was proposed for various influence factors based on the critical analysis of test results. The input of cement can be reduced by NRL replacement to attain the same target UCS0 and UCS(w-d), hence the reduction in carbon footprint. For the same UCS0 = 4.4 MPa, the carbon footprint of 4.4% cement stabilized soil was reduced to 30.7% as compared to the 3% cement and 20% NRL stabilized soil.
TL;DR: In this article, a set of experimental tests including standard compaction, unconfined compressive strength (UCS), and one-dimensional consolidation tests are performed to evaluate the effect of compaction delay on mechanical and consolidation characteristics of cement-stabilized subgrade soil.
Abstract: Soil compaction is a considerable construction activity to ensure safety and durability, notably in the transportation industry. Compaction delay (CD) occurs because of unpredictable cases which may be related to the climatic, environmental, and logistics reasons. Since the time delay in compaction process affects the engineering properties of the materials in road construction, the current study evaluates the effect of CD on mechanical and consolidation characteristics of cement-stabilized subgrade soil. To achieve this purpose, a set of experimental tests including standard compaction, unconfined compressive strength (UCS), and one-dimensional consolidation tests are performed. Samples are mixed with 1.5, 3, 6, and 9% Portland cement on their maximum dry density (MDD) considering different ranges of CD up to 120 min. Delayed samples show a reduction in MDD (3.46–5.43%), and UCS (11.31–37.25%) compared with those of non-delayed ones. Morphological findings from scanning electron microscope (SEM) analysis confirmed that the CD has a destructive effect on the mechanical characteristics of the soil–cement samples even in the long run. Correspondingly, delayed samples show lower secant modulus (1.81 times) rather than immediate compacted ones. Furthermore, higher CD yields mixtures with higher compression index and void ratio. Finally, laboratory test results are used to develop MDD and UCS regression models considering the CD parameter. The sensitivity analysis, based on regression models, shows that the MDD and UCS are noticeably influenced by cement content variations.
TL;DR: The significance of RAP amount on the resilient modulus behavior, shear strength and hydraulic conductivity characteristics of unbound granular base materials were investigated in this article, where RAP was blended with crushed aggregate by different percentages ranging from 0% to 100% by the blend weight.
Abstract: The application of reclaimed asphalt pavement (RAP) has become a common practice in road construction as a substitute to natural aggregate. The significance of RAP amount on the resilient modulus behavior, shear strength and hydraulic conductivity characteristics of unbound granular base materials were investigated in this research. RAP was blended with crushed aggregate by different percentages ranging from 0% to 100% by the blend weight. The laboratory testing program includes modified compaction, California Bearing Ratio (CBR), permeability, and repeated and static triaxial tests. A descriptive statistical analysis was conducted on all testing results. Furthermore, the X-ray computed tomography (CT) scanning technique was applied to investigate the internal (micro) structure of specimens. It was noted that with the addition of more RAP to the blend the resilient modulus increased and the coefficient of permeability decreased. The apparent cohesion of the RAP blends increased almost linearly and the friction angle decreased as the RAP replacement level increased. The CT scanning results indicated that the virgin crushed aggregate has a higher void ratio than the RAP blends, which interpreted the lower permeability and higher resilient modulus of RAP blends. In conclusion, blending RAP with virgin aggregate produces superior quality material for road bases.
TL;DR: In this paper, a field measurement was carried out in Shenzhen, China, where a double-line subway passes directly under the main urban road, and the rail acceleration at the straight track and the point of fixed frog in the tunnel as well as ground vibrations caused by double line subway and road traffic under different operating conditions were measured.
Abstract: Rail transit and highway transportation have flourished in the process of urbanization by virtue of their respective advantages, facilitating people’s travel but also producing harmful environmental vibrations. For an accurate analysis of source characteristics and propagation laws of train-induced vibrations, a field measurement was carried out in Shenzhen, China, where a double-line subway passes directly under the main urban road. The rail acceleration at the straight track and the point of fixed frog in the tunnel as well as ground vibrations caused by double-line subway and road traffic under different operating conditions were measured. A special attention was paid to the vibration response of each measurement point on the ground when the subway train and road vehicles passed through simultaneously. Measurement shows that horizontal acceleration cannot be ignored in the area close to the subway line, and vibration amplification phenomenon exists in the free field, due to the difference of the local geological conditions that leads to different vibration levels. Compared with the ground vibration caused by only the near-line train passage, when a near-line train passes the test section with a far-line train is about to arrive, the ground vibration intensifies in the frequency band of 50–63 Hz. When a far-line train passes the test section with a near-line train just left, the vibration level of each measuring point shows different degrees of reduction in various frequency bands, and reduction amount of horizontal acceleration in the frequency 10–16 Hz reaches 12 dB. Ground vibration aggravates significantly when the near-line train and bus pass simultaneously, the horizontal vibration is more sensitive to the superposition influence of road traffic. The measured data can not only guide the work of vibration environment assessment in the stage of subway design and planning but also can validate possible numerical models for predicting train-induced vibrations.
TL;DR: In this article, the use of alkali-activated slag for the development of road applications and how the interactions that occur between the binder and the organic matter originally present in the soil can strongly affect its reactivity in the process of stabilization and solidification.
Abstract: This paper presents novel findings regarding the use of alkali-activated slag for the development of road applications and, more particularly, how the interactions that occur between the binder and the organic matter originally present in the soil can strongly affect its reactivity in the process of stabilization and solidification. The study uses mechanical performances and macroscopic characterization, such as isothermal calorimetry and thermogravimetric analysis of the pure binders as well as of the soil-binder mixes in order to characterize the hydration mechanisms. By analyzing the chemical composition of organic matter extracted with three different alkaline activators, it is shown that both humic and fulvic acids are strong complexing agents, not only of calcium and aluminum ions, as noted in the existing scientific literature, but also of highly soluble silicon. In this study, only sodium hydroxide activated slag was found to be a suitable alkali-activated binder for subbase layer development.
TL;DR: In this paper, the engineering characteristics of expansive soils were investigated through an array of laboratory experiments on treated and untreated soil specimens mixed with various contents of additive and cured for different times and a comprehensive investigation of the microstructure evolution of soils after treatment was also undertaken using Fourier transform infrared and scanning electron microscopy techniques.
Abstract: Bagasse ash (BA) is an abundant industrial waste of the sugar-cane refining industry, and its improper disposal can result in a detrimental impact on the environment. In this investigation, BA is considered to assess the possible advantages of its pozzolanic component as a novel sustainable waste application for stabilisation of expansive soils. The engineering characteristics of expansive soils were investigated through an array of laboratory experiments on treated and untreated soil specimens mixed with various contents of additive and cured for different times. A comprehensive investigation of the microstructure evolution of soils after treatment was also undertaken using Fourier transform infrared and scanning electron microscopy techniques. The results revealed that addition of BA, lime, and in particular, combined BA-lime (BAL) remarkably improved the maximum strength (815%), the bearing capacity (9.2 times), the compressibility (83%), and the 100% swell properties of stabilised soils due to rich amorphous silica properties of BA waste that promoted higher pozzolanic reactivities of BAL-soil-mixtures and therefore, enhanced the engineering characteristics of treated soils. The findings showed that a proper combination of bagasse ash waste and lime, as a stabilising additive, can effectively enhance the engineering properties of expansive soil while addressing the environmental impact of BA waste disposal. The industrial waste (BA) can be reused as a cost-effective and green construction material for the benefit of sustainable development of civil infrastructure.
TL;DR: In this paper, four machine learning methods of KNN, Gaussian Process Regression (GPR), SVR, and Decision Tree (DT) were used for predicting the RQD status along the entire tunnel route.
Abstract: Machine learning (ML) is becoming an appealing tool in various fields of civil engineering, such as tunneling. A very important issue in tunneling is to know the geological condition of the tunnel route before the construction. Various geological and geotechnical parameters can be considered according to data availability to define tunnels' ground conditions. The Rock Quality Designation (RQD) is one of the most important parameters that are very effective in tunnel geology. This article aims to maximize the prediction accuracy of the RQD parameter along a tunnel route through continuous updating techniques. For this purpose, four ML methods of K-nearest neighbor (KNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) were considered. All the RQD observations along the tunnel route were considered as the models’ inputs. For predicting the RQD status along the entire tunnel route, the ML models use the regression technique. For checking the applicability of the models, the Hamru road tunnel in Iran was used. The models were updated twice to assess the update effect on the results achieved during the tunnel construction. In each prediction phase, all the prediction results were compared using different statistical evaluation criteria and the actual mode. Finally, the comparative tests' findings showed that predictions of the GPR model with R2 = 0.8746/root mean square error (RMSE) = 3.5942101, R2 = 0.9328/RMSE = 2.5580977, and R2 = 0.9433/RMSE = 1.8016325 are generally well-suited to actual results for pre-update, first update, and second update phases, respectively. The updating procedure also leads to prediction models that are more accurate and less uncertain than the previous prediction stage.
TL;DR: In this paper, the deformation behavior of two types of C&D waste materials, namely recycled concrete aggregate (RCA) and crushed brick (CB) when mixed with polyethylene terephthalate (PET) plastic waste was evaluated using repeated load triaxial (RLT) test.
Abstract: Recycling and reusing construction and demolition (C&D) wastes for civil engineering construction activities has been identified as an energy-saving and sustainable solution. The purpose of this study is to evaluate the deformation behavior of two types of C&D waste materials, namely recycled concrete aggregate (RCA) and crushed brick (CB) when mixed with polyethylene terephthalate (PET) plastic waste. RCA and CB were mixed with 1%, 3%, 5%, and 7% of PET, and the permanent deformation behavior of the blends was evaluated using repeated load triaxial (RLT) test. The shakedown criterion was utilized for identifying the deformation behavior of blends. Most of the PET/RCA and PET/CB blends exhibited Range B (plastic creep) and Range C (incremental collapse) response, respectively, in the investigated stress levels. Shakedown analysis of the test results indicated that up to 3% PET could be mixed with RCA for base/subbase applications, while CB should be mixed with 1% PET, in the subbase layer. Artificial neural network (ANN) method was next used to simulate the permanent strain and shakedown behavior of the blends with consideration of the physical properties and stress states. The ANN model was found to be highly efficient for simulating the permanent strain graph and identifying the shakedown behavior of the blends. A sensitivity analysis was subsequently performed to investigate the impact of input variables on the permanent deformation behavior and the results indicated that number of cycles and confining stress were the most important factors.