Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

This study evaluates the applicability of using a robust, novel, data-driven method in proposing surrogate models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio of coarse-grained soils using only the results of the grain size distribution analysis. The data-driven analysis has been conducted using evolutionary polynomial regression analysis (MOGA-EPR), employing a comprehensive database. The database included the particle diameter corresponding to a percentage of the passing of 10%, 30%, 50%, and 60%, coefficient of uniformity, coefficient of curvature, dry unit weight, optimum moisture content, and California bearing ratio. The statistical assessment results illustrated that the MOGA-EPR provides robust models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio. The new models’ performance has also been compared with the empirical models proposed by different researchers. It was found from the comparisons that the new models provide enhanced accuracy in predictions as these models scored lower mean absolute error and root mean square error, mean values closer to one, and higher a20−index and coefficient of correlation. Therefore, the new models can be used to ensure more optimised and robust design calculations

An experimental and numerical investigation on strengthening the upright component of thin-walled cold-formed steel rack structures

Cold-formed steel (CFS) racking systems are widely used for storing products in warehouses. However, as commonly used structures in storage systems, thin-walled open sections are subjected to stability loss because of various buckling modes, including flexural, local, torsional and distortional. This research proposes a novel technique to increase the ultimate capacity of uprights, utilising bolts and spacers, under flexural and compressive loads. The proposed components are attached externally to the sections in certain pitches along the length. In this regard, axial tests were performed on 72 upright frames and nine single uprights with various lengths and thicknesses. Also, the impact of using reinforcing elements was evaluated by investigating the failure modes and ultimate load results. It was concluded that the reinforcement technique is able to restrain upright flanges and therefore improve the upright profiles' strength. For testing the flexural behaviour, 18 samples of three types were made, including non-reinforced sections and two types of sections reinforced along the upright length at different pitches. After that, monotonic loading was applied along both the minor and major axes of the samples. The suggested reinforcing method leads to increasing the flexural capacity of the upright sections about both the major and minor axes. Also, by using reinforcing system, the flexural performance was improved, and buckling and deformation were constrained. In addition, the reinforcement technique was evaluated by Finite Element (FE) method. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms were deployed to predict the normalised ultimate load and deflection of the profiles. Following the empirical tests, the axial and flexural performance of different CFS upright profiles with various lengths, thicknesses and reinforcement spacings were simulated and examined. It was shown that the reinforcing technique improved the capacity of the samples. Consequently, the proposed reinforcements could be considered a highly effective and low-cost technique to strengthen the axial and flexural behaviour of open CFS sections considering a trade-off between performance and cost of utilising the approach

Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

Analytical asssessment of the structural behavior of a specific composite floor system at elevated temperatures using a newly developed hybrid intelligence method

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (δ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearson’s correlation coefficient, P = 2.22 × 10−32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications

An Experimental and Theoretical Study of Pile Foundations Embedded in Sand Soil

This study aimed to examine the load carrying capacity of model instrumented piles embedded in sand soil, and to develop and verify reliable, highly efficient predictive models to fully correlate the non-linear relationship of pile load-settlement behaviour using a new, self-tuning artificial intelligence (AI) approach. In addition, a new methodology has been developed, in which the most effective pile bearing capacity design parameters can be precisely determined. To achieve this, a series of comprehensive experimental pile load tests were carried out on precast concrete piles, steel closed-ended piles and steel open-ended piles, comprised of three slenderness ratios of 12, 17 and 25, using an innovative calibrated testing rig, designed and manufactured at Liverpool John Moores University. The model piles were tested in a large pile testing chamber at a range of different densities of sand; loose (18%), medium (51%) and dense (83%). It is worth noting that novel structural fibres were utilised and optimised for different volume fractions to enhance the mechanical performance of concrete piles. The obtained results revealed that the higher the values of the of the pile effective length, Lc (embedded length of pile), sand density, and the soil-pile angle of shearing resistance, the higher the axial load magnitudes to reach the yield limit. This can be attributed to the increase in the end bearing point and mobilised shaft resistance. In addition, the plastic mechanism occurring in the surrounding soil was identified as the leading cause for the presence of nonlinearity in the pile-load tests. Furthermore, a new enhanced self-tuning supervised Levenberg-Marquardt (LM) training algorithm, based on a MATLAB environment, was introduced and applied in this process. The proposed algorithm was trained after conducting a comprehensive statistical analysis, the key objectives being to identify and yield reliable information from the most effective input parameters, highlight the relative importance “Beta values” and the statistical significance “Sig values” of each model input variable (IV) on the model output. To assess the accuracy and the efficiency of the employed algorithm, different measuring performance indicators (MPI), suggested in the open literature, were utilised. Common statistical performance indexes, i.e., root mean square error (RMSE), Pearson’s moment correlation coefficient (p), coefficient of determination (R), and mean square error (MSE) for each model were determined. Based on the graphical and numerical comparisons between the experimental and predicted load-settlement values, the results revealed that the optimum models of the LM training algorithm fully characterised load-settlement response with remarkable agreement. Additionally, the proposed algorithm successfully outperformed the conventional approaches, demonstrating the feasibility of the current study. New design charts have been developed to calculate the individual contribution of the most significant pile bearing capacity design parameters “the earth pressure coefficient (K) and the bearing capacity factor (N )”. The improved approach takes into account the change in sand relative density, pile material type, and the pile slenderness ratios. It is therefore a significant improvement over most conventional design methods recommended in the existing design procedures, which do not consider the influence of the most significant parameters that govern the pile bearing capacity design process

Experimental and numerical investigation of an innovative method for strengthening cold-formed steel profiles in bending throughout finite element modeling and application of neural network based on feature selection method

This study evaluates an innovative reinforcement method for cold-formed steel (CFS) upright sections through finite element assessment as well as prediction of the normalized ultimate load and deflection of the profiles by artificial intelligence (AI) and machine learning (ML) tech-niques. Following the previous experimental studies, several CFS upright profiles with different lengths, thicknesses and reinforcement spacings are modeled and analyzed under flexural loading. The finite element method (FEM) is employed to evaluate the proposed reinforcement method in different upright sections and to provide a valid database for the analytical study. To detect the most influential factor on flexural strength, the “feature selection” method is performed on the FEM results. Then, by using the feature selection method, a hybrid neural network (a combination of multi-layer perceptron algorithm and particle swarm optimization method) is developed for the prediction of normalized ultimate load. The correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE) and Wilmot’s index of agree-ment (WI) are used as the measure of precision. The results show that the geometrical parameters have almost the same contribution in the flexural capacity and deflection of the specimens. According to the performance evaluation indexes, the best model is detected and optimized by tuning other algorithm parameters. The results indicate that the hybrid neural network can successfully predict the normalized ultimate load and deflection

Application of Machine Learning Technique in Predicting the Bearing Capacity of Rectangular Footing on Layered Sand under Inclined Loading

The aim of the present study is to apply machine learning technique to predict the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading. For this purpose, a total 5400 data based on the finite element method for the rectangular footing on layered sand under inclined loading were collected from the literature to develop the machine learning model. The input variables chosen were the thickness ratio (0.00 to 2.00) of the upper dense sand layer, embedment ratio (0 to 2), the friction angle of upper dense (410 to 460) sand and lower loose (310 to 360) sand layer and inclination (00 to 450) of the applied load with respect to vertical. The output is the ultimate bearing capacity. Further, the impact of the individual variable on the bearing capacity was also assessed by conducting sensitivity analysis. The results reveal that, the load inclination is the major variable affecting the bearing capacity at embedment ratio 0, 1 and 2. Finally, the performance of the developed machine learning model was assessed using six assessing statistical parameters. The results reveal that the developed model was performing satisfactorily for the prediction of the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.Forecasting ; Inflation (Finance) ; Monetary theory

Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions; an intelligent evolutionary approach

The complex behaviour of fine-grained materials in relation with structural elements has received noticeable attention from geotechnical engineers and designers in recent decades. In this research work an evolutionary approach is presented to create a structured polynomial model for predicting the undrained lateral load bearing capacity of piles. The proposed evolutionary polynomial regression (EPR) technique is an evolutionary data mining methodology that generates a transparent and structured representation of the behaviour of a system directly from raw data. It can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed model allows the user to gain a clear insight into the behaviour of the system. Field measurement data from literature was used to develop the proposed EPR model. Comparison of the proposed model predictions with the results from two empirical models currently being implemented in design works, a neural network-based model from literature and also the field data shows that the EPR model is capable of capturing, predicting and generalising predictions to unseen data cases, for lateral load bearing capacity of piles with very high accuracy. A sensitivity analysis was conducted to evaluate the effect of individual contributing parameters and their contribution to the predictions made by the proposed model. The merits and advantages of the proposed methodology are also discussed