Bank Instability Problems Associated With the Riverside Construction

In this case study, the sliding of a riverbank during construction of a water intake facility in Tennessee was investigated and analyzed. The construction of the project involved the installation of 2-36 in. diameter intake pipes from the wet well to the river inlet which were 290 ft apart. An open cut excavation from the river inlet to the riverbank toe was used to connect the inlet to the tunnel-installedintake pipes on the land side. During the excavation, a 25 ft wide slide, which 4 months later widened by another 15 ft, developed to the crest of the road on the riverbank. Consequently, a concern developed for the safety of the roadway. The geometry of the slopes and the cuts, pre- and post-construction geotechnical subsurface investigation, construction history, and sliding conditions were examined for the causes of the riverbank instabilities. The fundamental cause of the slides was the undermining of the latent bedrock surface from subaqueous excavation into the riverbank

Learning of Soil Behavior from Measured Response of a Full Scale Test Wall in Sandy Soil

In urban deep excavations, instruments are placed to monitor deformations and to control construction and reduce the risk of excessive and potentially damaging deformations. The second author has introduced a new inverse analysis approach that utilizes measured excavation performance to extract the underlying soil behavior. The extracted soil behavior can be used in predicting the behavior of similar excavations. This paper provides a first implementation of this inverse analysis approach to a well instrumented full scale test wall in a sand deposit. A wall consisting of soldier beams with wood lagging was instrumented to study anchored (one and two level tie backs) wall behavior in sandy soil deposits at Texas A&M. Strain gauges, load cells, inclinometers, and settlement points were placed in two sections of the excavation to monitor the excavation behavior. The measured excavation response for the section with two-level tie-backs is used to extract the constitutive model through the inverse analyses approach. The extracted constitutive model is used in predicting the underlying soil behavior for the section with one tie-back level. The predicted behavior of the excavation and its agreement with measurements at the site are discussed in detail

Plasticity Requirements of Aggregates Used in Pavement Base and Subbase Courses

In pavement unbound aggregate layers, fines content (passing No. 200 sieve size or finer than 0.075 mm) characteristics influence the aggregate matrix strength and the modulus and deformation behavior. A laboratory investigation was conducted to identify the effects of fines content, plasticity index, dust ratio (percent passing No. 200 to No. 40 sieve size), and gradations on the strength and the modulus and deformation characteristics of crushed gravel and limestone aggregates. A series of moisture-density and California Bearing Ratio (CBR) tests were conducted on considered configurations. Furthermore, triaxial strength and resilient modulus tests were conducted on selected samples. A series of guide charts are presented to show the effects of various fines content characteristics on the strength and the modulus and deformation behavior of aggregates. Some of the configurations that are in compliance with existing IDOT specifications provided unacceptable strength values. For example, the use of aggregates with low dust ratio and high fines content resulted in a weak aggregate matrix. In general, the detrimental effect of a high plasticity index is more pronounced on crushed gravels. The findings of this study relates to the IDOT SSRBC Article 1004.04 specification. For any modification to be applied to this specification, it is recommended that these laboratory results be further validated using field or full-scale tests.IDOT-R27-157Ope

Effective Post-Construction Best Management Practices (BMPs) to Infiltrate and Retain Stormwater Run-off

Performance analyses of newly constructed linear BMPs in retaining stormwater run-off from 1 in. precipitation in post-construction highway applications and urban areas were conducted using numerical simulations and field observation. A series of simulations were conducted using an idealized catchment on a four-lane highway located on sites with soil types ranging from clayey to sandy material across state of Illinois. The use of turfgrasses and prairie grass vegetative surface covers in pre-BMP scenarios in promoting infiltration and reducing stormwater run-off were investigated. Three types of BMPs—bioswale, infiltration trench, and vegetated filter strips—as well as combinations thereof, were studied for determining their ability to control stormwater run-off in an idealized catchment. This report also documents the maintenance cost, construction cost, and life-cycle analyses of those BMPs to identify cost-effective solutions. The effects of erosion and a sediment accumulation rate of 1 t/ac/y on BMPs during the 2- year and 10-year lifespans of bioswales and infiltration trenches were studied using full-scale field tests. The simulation and field test results provide insight for developing guidelines for cost-effective BMPs to control stormwater run-off in linear projects.IDOT-R27-141Ope

The interplay between field measurements and soil behavior for learning supported excavation response

In urban areas, estimation of ground movements due to excavation is critically important. In this thesis after a short review of currently used methods in practice for estimating excavation induced ground movements, a novel inverse analysis approach, self-learning in engineering simulation (SelfSim), is presented. SelfSim is applied to deep excavations in order to extract underlying soil behavior. The performance of the SelfSim inverse analysis is compared to inverse analysis based on a genetic algorithm. In the SelfSim approach, soil behavior is extracted from in situ measurements without a pre-defined constitutive model. In the genetic algorithm approach, soil parameters of an existing constitutive model are identified using field measurements. The performance of both techniques in capturing soil displacements and in predicting of soil behavior associated with the Lurie Center excavation in Chicago is presented. In order to demonstrate SelfSim???s capabilities in learning soil behavior using different instrument measurements, a simulated deep excavation is analyzed. The quality of the extracted behavior is examined by deploying different instrument configurations. The instruments required to provide sufficient information for SelfSim to extract soil behavior are identified. Then, some of the findings are further demonstrated in a case study of an excavation in Taipei soft clays. To illustrate that it is possible to learn from local experience and predict excavation performance in similar soil stratigraphy, case studies in Texas, Shanghai and Taipei are analyzed. The difficulties associated with the use of measured excavation response that is incompatible with recorded construction activity and the importance of engineering judgment in preparing measurement data for inverse analysis are highlighted. Finally, it is shown that the 2D extracted soil behavior of excavation in Chicago clays can not provide a reasonable excavation performance for an elevated ground surface excavation in Chicago suburbs within similar soil stratigraphy. It is demonstrated that the 3D effects of excavation are captured via 3D modeling using SelfSim. At the end, the extracted soil behavior from 3D analysis is discussed and compared to extracted soil behavior from 2D analysis

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models

A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability

Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of this study is to introduce a novel metaheuristic optimization namely Harris hawks’ optimization (HHO) for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations. In this way, four slope stability conditioning factors, namely slope angle, the position of the rigid foundation, the strength of the soil, and applied surcharge are considered. Remarkably, the main contribution of this algorithm to the problem of slope stability lies in adjusting the computational weights of these conditioning factors. The results showed that using the HHO increases the prediction accuracy of the ANN for analysing slopes with unseen conditions. In this regard, it led to reducing the root mean square error and mean absolute error criteria by 20.47% and 26.97%, respectively. Moreover, the correlation between the actual values of the safety factor and the outputs of the HHO–ANN (R2 = 0.9253) was more significant than the ANN (R2 = 0.8220). Finally, an HHO-based predictive formula is also presented to be used for similar applications

Comparison of two inverse analysis techniques for learning deep excavation response

Performance observation is a necessary part of the design and construction process in geotechnical engineering. For deep urban excavations, empirical and numerical methods are used to predict potential deformations and their impacts on surrounding structures. Two inverse analysis approaches are described and compared for an excavation project in downtown Chicago. The first approach is a parameter optimization approach based on genetic algorithm (GA). GA is a stochastic global search technique for optimizing an objective function with linear or non-linear constraints. The second approach, self-learning simulations (SelfSim), is an inverse analysis technique that combines finite element method, continuously evolving material models, and field measurements. The optimization based on genetic algorithm approach identifies material properties of an existing soil model, and SelfSim approach extracts the underlying soil behavior unconstrained by a specific assumption on soil constitutive behavior. The two inverse analysis approaches capture well lateral wall deflections and maximum surface settlements. The GA optimization approach tends to overpredict surface settlements at some distance from the excavation as it is constrained by a specific form of the material constitutive model (i.e. hardening soil model); while the surface settlements computed using SelfSim approach match the observed ones due to its ability to learn small strain non-linearity of soil implied in the measured settlements

Effect of Sediment Accumulation on Best Management Practice (BMP) Stormwater Runoff Volume Reduction Performance for Roadways

Best management practices (BMPs) are commonly used to reduce the adverse effects of post-construction runoff. BMP deterioration happens over time when these age and the infiltration rate decreases as a result of sediment accumulation. The objective of this paper was to investigate the effect of sediment accumulation on BMP stormwater runoff volume reduction performances. The BMPs studied included a bioswale and an infiltration trench. To undertake this research, both field tests and numerical simulations were conducted under five different and single rainfall events with a wide range of intensities and duration. The minimal sediment accumulation of 0.22 kg/m2·year was considered in this study. Three different sedimentation accumulation configurations (i.e., new, 2-year-old, and 10-year-old BMPs) were considered. According to the results, the infiltration trench had 100% runoff reduction efficiency in all conditions including high-intensity rain and 10-year-old BMP age. The performance of the bioswale for the first 2 and 10 years deteriorated by about 55% and 70%, respectively

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models