In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.
Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation
Accurate prediction of the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management systems. However, the expensive measurement equipment and interference with the traffic flow during the tests are reported as the challenges of the assessment of RSL of pavement. This paper presents a novel prediction model for RSL of road pavement using support vector regression (SVR) optimized by particle filter to overcome the challenges. In the proposed model, temperature of the asphalt surface and the pavement thickness (including asphalt, base and sub-base layers) are considered as inputs. For validation of the model, results of heavy falling weight deflectometer (HWD) and ground-penetrating radar (GPR) tests in a 42-km section of the Semnan–Firuzkuh road including 147 data points were used. The results are compared with support vector machine (SVM), artificial neural network (ANN) and multi-layered perceptron (MLP) models. The results show the superiority of the proposed model with a correlation coefficient index equal to 95%.
In order to maintain, manage, and budget for pavement infrastructure, road pavement condition assessment is necessary. Several pavement characteristics are measured to assess its condition, including pavement strength, roughness, and surface distresses. It is important to categorize studies at deeper levels due to the rapid growth of articles published in this field. The objective of this paper is to provide an overview of machine learning-based pavement evaluation studies and their contributions to the area. In order to facilitate the exploration of the studies employing similar methodologies, the studies are organized based on their goals. Therefore, studies are classified based on the two main categories of goals employed in them, namely: 1. Studies with aim of pavement condition prediction and 2. Studies with the aim of pavement distress detection/classification. It is observed that research of category 1 has grown very well during the past years. Also, category 2 includes studies that mostly focus on crack detection and it can be felt that there is a need for expanding the focus of studies on other types of distresses.
New numerical models are developed to predict the strength of concrete under multiaxial compression using linear genetic programming (LGP). The models are established based on a comprehensive database obtained from the literature. To verify the applicability of the derived models, they are employed to estimate the strength of parts of the test results that are not included in the modeling process. The external validation of the model is further verified using several statistical criteria. The results obtained by the proposed models are much better than those provided by several models found in the literature. The LGP-based equations are remarkably straightforward and useful for pre-design applications.
No abstract is provided for this article.
No abstract is provided for this article.
This paper presents an empirical model to predict the shear strength of RC deep beams. A hybrid search algorithm coupling genetic programming (GP) and simulated annealing (SA), called genetic simulated annealing (GSA), was utilized to develop mathematical relationship between the experimental data. Using this algorithm, a constitutive relationship was obtained to make pertinent the shear strength of deep beams to nine mechanical and geometrical parameters. The model was developed using an experimental database acquired from the literature. The results indicate that the proposed empirical model is properly capable of evaluating the shear strength of deep beams. The validity of the proposed model was examined by comparing its results with those obtained from American Concrete Institute (ACI) and Canadian Standard Association (CSA) codes. The derived equation is notably simple and includes several effective parameters.
Concrete, as one of the essential construction materials, is responsible for a vast amount of emissions. Using recycled materials and gray water can considerably contribute to the sustainability aspect of concrete production. Thus, finding a proper replacement for fresh water, in the production of concrete, is significant. The usage of industrial wastewater, instead of water in the concrete can be considered in this paper. In this study, 450 concrete samples are produced with different amounts of wastewater. The mechanical parameters such as slump, compressive strength, water absorption, tensile strength, electrical resistivity, rapid freezing, half-cell potential, and appearance are investigated. The results showed that the usage of industrial wastewater does not significantly change the main characteristics of concrete. Although, increasing the concentration of the wastewater can decrease durability and strength features nonlinearly.
No abstract is provided for this article.
Ground deformation, due to tunneling, is one of the most significant challenges in tunnel design in soft ground along with, the predicting the related effects of tunneling on nearby structures.One of the methods of predicting ground settlement in tunneling projects, is to use analytical and numerical methods.By measuring the amount of settlement with accurate instruments and back-analysis of behavioral measurement data, in addition to estimating the state of settlement of the ground and surrounding structures, it is possible to determine the geotechnical parameters of the soil and structure in the design of upcoming sections and future designs.In this study, an attempt has been made to verify the measured settlements caused by digging the tunnel of an urban train line, by using back analysis.For this purpose, comparisons with predictions obtained from empirical and analytical methods and the Geotechnical Engineering Finite Element Analysis software (PLAXIS) was used.The results show that often, the empirical methods obtain values more than the measured values, for ground settlement.
Driving quality is a concept that includes factors affecting the quality of a vehicle trip. Pavement damages, including roughness, has negative effects on travel comfort and driving quality. The vibration created in the car is one of the most important negative effects. In general, the main reasons for this type of vibration are: The mechanical structure of the vehicle and the surface roughness of the road. In short trips, the quality of driving is affected by safety and driving comfort indicators. While, in long-term trips, the threat to human health is also raised due to long-term vibration on the body. Therefore, it can be concluded that the evaluation of the driving quality and especially the impact of road surface roughness, which is responsible for the phenomenon of vibration, is a vital issue for pavement engineers. As the most common method of assessing road surface roughness, the IRI index is considered an essential element in the framework of this research due to its intrinsic relationship with the vehicle's reaction. This study seeks to reconsider the IRI index according to the vibration created in the car during a trip. For this purpose, first, some pavement segments with certain roughness were defined. Then, with the help of ProVAL software, IRI values were calculated for them. After that, in the main part of the research, the defined pavement segments were made in the ADAMS software, and the vibration results were outputted when a passenger car passed over them. The results of the review and comparison of vibration data and IRI values showed that the IRI method contains an important problem: not fully covering the concept of passenger travel comfort and driving quality. Considering that the IRI index is an important and common factor in the decisions of pavement management systems around the world, this research confirms that this index needs important modifications to effectively include the concept of driving quality in roads' maintenance and repair programs.
N-2-pyrazinyl-2-furancarboxamide (I) and N-2-pyrazinyl-2-thiophenecarboxamide (II) are compounds containing different five-membered heteroaromatic rings, furan and thiophene, respectively. They were designed and synthesized to examine the effect of an increase in aromaticity from furan to thiophene on the crystal packing. In order to explore the various features of the crystal packing motifs in more detail, single crystal X-ray diffraction, Hirshfeld surface analysis and theoretical calculations were carried out on the two compounds. The results clearly show that the heteroatom substitution of O to S in five-membered rings led to an increase in the effectiveness of π-based interactions in II, whereas hydrogen bond interactions play a more important role in the stabilization of the supramolecular architecture of I.
No abstract is provided for this article.
Urban train infrastructures are very important for reliable urban mobility.This paper proposes a three-dimensional modeling of mechanized drilling corridors.Drilling in urban areas is always a risky and complex project.One of the most important issues during the construction of subway tunnels is the investigation of the impact of drilling steps on the ground subsidence and impact on existing structures.For this purpose, different types of mechanized drilling methods are often used, resulting in a considerable reduction in the displacements caused by tunnel drilling.In this study, part of the route of an urban train tunnel, that passes under a traffic interchange, is examined.The shear strength capacity of the slab pile was calculated, using the relevant equations, and then, the modeling of the soil mass was performed, using the PLAXIS 3D finite element program.The proposed depth of the tunnel construction, by the consulting company, is 18 meters.Due to drilling problems, D. Mohammadzadeh S. et al.Three-Dimensional Modeling and Analysis of Mechanized Excavation for Tunnel Boring Machines -214 -a depth of 14 meters has been suggested as an alternative.Analysis of both the depths of 14 and 18 meters, showed that the displacements at both depths, were approximately the same.However, the impact of the tunnel, on the capacity of the piles' tip, at a depth of 18 meters, is greater than at the depth of 14 meters.Thus, the suggested optimum depth is 14 meters, which is more suitable, than the initial suggested depth of 18 meters.
The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.
