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In recent decades, the bender element (BE) test has been used to estimate the seismic wave velocity in the laboratory due to several advantages; simplicity, versatility, fastness, inexpensiveness, and non-destructive nature. However, even with the advanced usage of BE in the laboratory, there has been little effort to use the BE in the field. In this article, the BE was used on a physical model at a multilayer mixture soils system and using different methods, patterns, and wave path lengths to evaluate the BE technique in the simulated field. The results indicated that the cross-hole pattern was the most suitable pattern to implement the BE test on the simulated field. BE results were highly influenced by the boundary condition when the distance between the sensor and hard boundary is less than 0.3 of the wave path length. BE sensors were able to detect seismic wave velocity at a ratio of the wave path length to sensor length up to 200 times.

Bender element (BE) is a useful seismic tool to predict the geotechnical soil properties using empirical correlations. However, there were uncertainties in the correlation equations which were not discussed in detail. In this study, the seismic data was thoroughly investigated to improve the correlations with the physical soil properties. Several proportions of sand–kaolin mixtures were compacted, sheared, as well as BE tested. The results showed curve relationships whereas the highest seismic wave velocity and the highest shear strength were attained at the proportion of 40% fine content. At this point, the void ratio, intergranular void ratio, maximum dry density, and specific gravity were 0.43, 1.43, 1.79 g/cm 3 , and 2.585 respectively. In addition, a direct positive linear relationship between the seismic wave velocity with cohesion (c) and shear strength (τ) provided highest values of seismic velocities at 53.7 kPa and 81.7 kPa of c and τ respectively. However, the seismic wave velocity less significant effected by the friction angle. The paper presented 68 empirical correlation equations between the previous parameters. The comparison with previous researchers indicated that the application of the empirical correlation equations was limited to the material type (clay and sand), fine content range (20–70%), void ratio range (> 0.43), and condition of the samples.

Geotechnical maps provide preliminary knowledge of sub-surface parameters which help in hazard identification, planning of detailed investigations, mitigation measures, and design for engineering projects. Maps consisting of zones with generalized stratigraphy for an entire area provide uncertain information which may lead to overlooking subsurface geotechnical hazards. In contrast, continuous contour plots consider spatial variation with depth. The purpose of this paper is to make efficient SPT N-value digital maps and soil type maps classified using Unified Soil Classification System (USCS) that indicate N value and soil type at unsampled locations in Islamabad, Pakistan. The geostatistical Kriging approach is unprecedently applied to Islamabad (Pakistan) to develop the contours. The proposed methodology involves integrating geotechnical data with contouring software Surfer 18. Data from fifty geotechnical investigation reports were used to create contour plots of twelve soil types and SPT N values at 3, 5, 10, 15, 20, and 25 ft depth intervals. In addition, a map indicating the depth of groundwater was also developed. The geotechnical contour maps presented in this paper are the first of their kind for Islamabad and lie consistent with geological generalizations of the region. A coefficient of correlation of 0.88 was found for SPT N-values. The output will serve as a supplement for site characterization and hazard identification for future projects.

This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL 200 is considered the accurate representation of plasticity of soil, its’ determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL 200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL 40 ) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.

The American Society of Civil Engineers set three pillars of sustainability, the triple bottom line approach, revolving around the environment, economy and equity. This approach is aligned with the Sustainable Development Goals set by the United Nations. Activities undertaken in any construction project must follow this approach and must be audited to validate their impact on sustainability. Geotechnical projects lack an audit/assessment tool encompassing the triple bottom line. Efforts were made to modify SPeAR (Sustainable Project Appraisal Routine) into Geotechnical SPeAR, but the system lacks the quantification scale as used by Environmental Geotechnics Indicators. The study aims to develop a new tool called Geo-SAT (Geotechnical Sustainability Assessment Tool), overcoming these limitations, incorporating engineering as a vital pillar. Geo-SAT is based on indicators quantified on a scale of 1 (detrimental) to 5 (significantly improved) to assess the impact of actions taken or considered, on sustainability. The total number of indicators developed is 169 out of which 79 are specific to the triple bottom line approach and 90 to engineering. These indicators are generic and can be used for geotechnical projects with the flexibility of exclusion as per the nature of the project. The different fields targeted are dams, foundations, landslides, contaminated site remediation, soil and erosion control, offshore construction and transportation. This tool will serve as a potential code of sustainability for geotechnical projects.

In the bender element (BE), the variations in the recommended Ltt/λ (wave path length, Ltt and the wavelength, λ) raise questions about the effect of these ratios in the near-field effect. The objectives of this study were sought to verify the efficiency of the Ltt/λ on the near-field effects as well as filling the gap associated with the assessment boundary conditions in both free/flexible and rigid boundaries. To achieve these objectives, the BE technique was implemented in uniform material (polystyrene) to assess both effects using various excitation frequencies, thicknesses, ratios of specimen width (D) to wave path length (Ltt), Ltt/λ, and time-domain interpretation methods. The results showed that both free/flexible and rigid boundary had no significant effects on compression wave velocity while the shear wave velocity was subjected to variation at D/Ltt < 1.15 and D/Ltt < 0.77 in free/flexible and rigid boundary respectively. At the rigid boundary, the reflected compression wave obscured the first-peak of direct shear wave at D/Ltt < 0.77. The outcomes from this study and the variations in the suggested Ltt/λ from previous researchers using different materials indicated that the near-field effects do not entirely depend on Ltt/λ. Nevertheless, the detection of the arrival time is affected by the variation in the frequency. Conclusively, to avoid the effect of boundary conditions, no measurements should be conducted via BE if the D/Ltt is less than 0.77. The first-peak method (using the conditions of Ltt/λ ≥ 5) was recommended to reduce the variations in the results as well as near-field effects. The longer the wave path, the lower the variation in the analysis of the wave velocity.

This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL200 is considered the accurate representation of plasticity of soil, its’ determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL40) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.

Sustainability is the ability of the system to retain and survive its functionality with time. A system is sustainable as long as the capacity (supply) is greater than the load (demand). Along the timeline of the project, the options to ensure sustainability minimize inferring better planning considerations at early stages. Geotechnical engineering, being the opening phase of any construction project, can contribute most to attain sustainability goals for all aspects, i.e. engineering, environmental, economic, and equity (4Es). Through a survey and available literature, it has been concluded that geotechnical engineering lacks a dedicated sustainability assessment tool/technique. Different assessment techniques/tools and technical aspects of geotechnics were studied to develop Geo-SAT (Geotechnical Sustainability Assessment Tool). Geo-SAT is developed to ensure the lack of research encompassing global sustainability goals. Based on quantifiable indicators assessed by a third party on a scale of 1 (detrimental) to 5 (significantly improved) measuring the impact on sustainability for each decision made, a total of 171 indicators were developed (all generic in nature) with the flexibility of addition, exclusion, and/or modification as per project’s nature. The engineering aspect is developed using 92 indicators, 27 retained and 8 modified from Environmental Geotechnics Indicators (EGIs), and 57 new. The different fields targeted are dams, foundations, landslides, contaminated site remediation, soil and erosion control, offshore construction, and transportation.

The interpretation methods used to predict ultimate pile capacity from load-settlement curves yield varied results due to their unique assumptions and limitations. Misinterpreting ultimate pile capacity can negatively impact the safety and economy of deep foundations, which often consist of numerous piles. This study examines a database of 93 static pile load tests on axially loaded bored concrete piles to identify suitable methods for predicting ultimate pile capacity. Specifically, 43 static pile load tests from Pile Database A were analyzed to compare ultimate capacities derived from theoretical methods, twelve pile load test interpretation techniques, and numerical analysis using PLAXIS 3D. For the numerical analysis, all bored piles in Database A were loaded until a pile head settlement equal to 8% of the pile diameter, which is the average failure settlement percentage derived from 50 full-scale static pile load tests (Database-B). The results indicate that the interpretation methods of DeBeer and Mazurkiewicz provide results closest to the maximum test load in static pile load tests, with an average deviation of 4.16% from the maximum applied test load. Therefore, these methods are reliable for predicting the ultimate capacity of bored piles. The findings of this study are expected to help design professionals adopt suitable and reliable methods for accurately assessing the ultimate capacity of bored piles.

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature.