Morris Sensitivity Analysis Research Articles

Prediction model of maximum stress for concrete pipes based on XGBoost-PSO algorithm

Due to varying burial conditions and service environments, concrete pipes exhibit complex mechanical behavior. Existing theoretical, simulation, and test methods for determining the maximum stress of pipes have significant limitations, including excessive assumptions, low efficiency, and restricted applicable conditions. Therefore, there is an urgent need to propose a multi-factor associated method for pipe stress prediction. This paper proposed an innovative approach to finely simulate the mechanics response for concrete pipes under the coupling action of stress-seepage-fluid fields. The accuracy of this numerical simulation method was validated through full-scale test. Sensitivity analysis based on Morris sensitivity analysis theory was conducted on traffic load magnitude and speed, buried depth, groundwater table, fluid height and velocity, bedding strength, backfill strength, and pipe diameter. A dataset of “physical variables - maximum stress” for concrete pipes was established. A multi-factor-correlated prediction model of maximum stress for concrete pipe was proposed using XGBoost machine learning method optimized by Particle Swarm Optimization (PSO) algorithm. The results indicate that pipe diameter is highly sensitive; buried depth and traffic load magnitude are sensitive; groundwater table, bedding strength, and backfill strength are moderately sensitive; while fluid height, traffic speed, and flow velocity are insensitive. The XGBoost-PSO model demonstrates the highest accuracy and lowest error compared to BP, RF, and XGBoost models, with improvements in prediction accuracy of 59.6 %, 23.8 %, and 8.6 %, respectively. The model achieves RMSE of 0.118 and MAE of 0.213, demonstrating the suitability of the XGBoost-PSO model for predicting maximum stress for concrete pipes.

Numerical investigation for shear behavior of pretensioned spun precast concrete pile

Pretensioned spun precast concrete (SPC) pile has emerged as a popular solution for deep foundation systems due to its ease of construction, enhanced bearing capacity, and overall cost-effectiveness. However, the hollowness of SPC piles, which reduces their sectional area, may become critical near the pile cap zone, especially in liquefaction scenarios during earthquake events. This study numerically investigates the shear behavior of hollow pre-tensioned SPC piles using the Finite Element (FE) technique. Primarily, the numerical results are validated with the test results of actual SPC piles where they were manufactured by high-strength concrete and high-tensioned steel strands for both with and without spirals. The numerical results show a good agreement with the experimental data in terms of strength, stiffness, and failure pattern for both types of spiral arrangements. The numerically obtained shear capacity is also found to be within close range of the code recommendations. A series of parametric studies have been conducted to understand the influence of essential pile parameters such as spiral’s spacing, effective prestress, number of strands, thickness-to-diameter ratio, concrete strength, and test orientation angle on the shear resistance of SPC pile. The study claimed that the addition of shear reinforcements to SPC piles boosts their shear capability, whereas the addition of strands and angle of test orientation have little effect on the piles' shear resistance. The optimum applied pre-stressing was found in a range of 4–8 MPa. The optimum values of t/d ratios in terms of material savings to shear resistance of the SPC pile fall in a range of 0.20 – 0.30. Morris Sensitivity Analysis (MSA) reveals that effective prestress and pile concrete strength are the most influential parameters of the shear capacity of SPC piles. This study provides important details for the SPC pile's shear design, illustrating its applicability in deep foundations in seismic areas.

A Morris sensitivity analysis of an office building's thermal design parameters under climate change in sub-Saharan Africa

Sub-Saharan Africa faces severe overheating problems during hot periods due to substandardly constructed buildings and high-priced energy services. Global warming is expected to aggravate the situation. Although literature addresses the effects of climate change on buildings in different world regions, how it will impact the Sub-Saharan region is still being determined, particularly in terms of design parameters and how they will vary. This study assesses the influence of design parameters on the cooling energy demand of a small-scale office building in Lagos and Kano, Nigeria. A Morris sensitivity analysis was carried out to rank and compare the influence of different design parameters on energy consumption, determined using EnergyPlus for present-day typical weather and the SSP5-8.5 scenario. The future scenario was generated using the Future Weather Generator, a morphing tool. The results show that, in 2080, the cooling load will increase from 1551 kWh/a to 2612 kWh/a (68.4 %) in Kano and from 1931 kWh/a to 3093 kWh/a (60.1 %) in Lagos. The cooling load in Lagos will generally be higher than in Kano by 18.4 % (596 kWh/a). The results indicate that reductions in the thermal conductivity of the east wall and decreases in the solar absorptance of the east wall, roof, and west wall elements will be the most significant factors affecting cooling in Kano. In Lagos, reductions in the thermal conductivity of the east wall and decreases in solar absorptance of both the east wall and roof elements will be the most influential.

Impact of vegetation composition and seasonality on sensitivity of modelled CO2 exchange in temperate raised bogs

Encroachment of vascular plants (VP) in temperate raised bogs, as a consequence of altered hydrological conditions and nutrient input, is widely observed. Effects of such vegetation shift on water and carbon cycles are, however, largely unknown and identification of responsible plant physiological traits is challenging. Process-based modelling offers the opportunity of gaining insights into ecosystem functioning beyond observations, and to infer decisive trait shifts of plant functional groups. We adapted the Soil–Vegetation–Atmosphere Transfer model pyAPES to a temperate raised bog site by calibration against measured peat temperature, water table and surface CO2 fluxes. We identified the most important traits determining CO2 fluxes by conducting Morris sensitivity analysis (MSA) under changing conditions throughout the year and simulated VP encroachment. We further investigated transferability of results to other sites by extending MSA to parameter ranges derived from literature review. We found highly variable intra-annual plant traits importance determining ecosystem CO2 fluxes, but only a partial shift of importance of photosynthetic processes from moss to VP during encroachment. Ecosystem respiration was dominated by peat respiration. Overall, carboxylation rate, base respiration rate and temperature sensitivity (Q10) were most important for determining bog CO2 balance and parameter ranking was robust even under the extended MSA.

Optimisation of spool offset of sleeve valve considering opening frequency

ABSTRACT The sleeve valve is a crucial component of the integrated water distributor utilised in offshore oilfields for waterflooding. Spool offset is highly correlated with sticking. However, the existing design does not consider the opening frequency, leading to elevated offset and deviating from the design goal. Therefore, a novel design method is proposed herein. The frequency of operation opening is categorised into the primary and the secondary operating conditions. The sensitivity of design parameters was identified using Morris sensitivity analysis, and genetic algorithms were used to optimise target variables for different operating conditions. The simulation results show that an average of 65.06% reduces the offset, and an average of 19.5% increases the mass flow rate. In particular, the offsets are near zero under the primary working condition. This approach can offer significant guidance for valve design under unique operation conditions of openings.

Experimental study on storage performance of packed bed solar thermal energy storage system using steel slag

Renewable energy sources are challenging due to their intermittent and unstable power output. However, Thermal Energy Storage (TES) systems offer an effective solution to address this issue. Among the available heat storage options, the air-packed bed is widely preferred in heat storage due to its cost-effectiveness and stability. This study investigates the feasibility of utilizing steel slag, a by-product of the steel industry, as the heat storage material in an air-packed bed. The research examines the flow characteristic, thermal performance, and heat storage properties of Packed Bed Thermal Energy Storage (PBTES) systems. Additionally, the study explores the impact of different heat storage temperatures and inlet air flows on the performance of PBTES and finds that Steel slag exhibits promising potential for broad applications in solid thermal storage. Finally, a Morris sensitivity analysis based on Latin Hypercube Sampling (LHS) is conducted. The findings demonstrate that the specific heat and density of air significantly influence the calculation of heat storage. Therefore, to achieve high accuracy in the analysis, it is crucial to consider the variations of these two parameters with temperature and pressure.

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model.

Sensitivity Analysis and Multi-Objective Optimization of Skylight Design in the Early Design Stage

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.

Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment

Rapid economic growth and urbanization have significantly changed the land use distribution and landscape ecological structure, which has a profound impact on the natural environment. A scientific grasp of the characteristics of land use distribution and its impact on landscape ecological risk is a prerequisite for sustainable urban development. This study aimed to calibrate GlobalLand30 data using the normalized difference impervious surface index (NDISI) obtained from Landsat images, thereby providing a more precise foundation for land simulation. Additionally, it sought to improve the accuracy of the patch-generating land use simulation (PLUS) through parameter sensitivity analysis. Building upon this, the research also simulates future land use in Beijing. Lastly, this study introduced an LER index to assess ecological risk in the current and future urban landscapes. The results showed that the GlobalLand30 data were calibrated and PLUS model accuracy was improved to more than 86%. The accuracy of the modified PLUS model based on a Morris sensitivity analysis was increased, and the kappa coefficients were increased by approximately 3%. The results of the multi-scenario simulation showed that under the SSP126-EP scenario, future land use in Beijing could balance urban development and ecological protection, and thus would be more suitable for sustainable development. In the other two scenarios, ecological land will be encroached by urban development. From 2000 to 2020, the degree of LER was generally lower, moderate, or higher, and the overall level of LER showed a downward trend continuing until 2100 in the SSP126-EG scenario. Future land use simulations and LER assessment under multi-scenarios could help decision makers develop multi-scale landscape protection strategies.

Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis

A methodology to determine the most influential independent input variables on the maximum power generation of an automotive thermoelectric generator is developed. A validated numerical model is used to predict the maximum power generation of a thermoelectric generator composed of several thermoelectric modules installed in the exhaust pipe of an internal combustion engine. The Morris sensitivity analysis and the least absolute shrinkage and selection operator feature selection approach are employed to identify the most influential variables. The amount of independent variables selected to carry out the analysis are 18 and they are embedded in different fields such as hydraulic, thermal, electrical, chemical, geometrical and design. Results show that the most influential variables are the inlet temperature of the hot fluid and the Seebeck coefficient and electric resistance of the thermoelectric modules. The thickness of the thermoelectric modules has the least influence on the maximum power generation. These findings could be useful to other researchers to develop simpler mathematical models without compromising the accuracy.

Comparative performance evaluation of multi-objective optimized desiccant wheels coated with MIL-100 (Fe) and silica gel composite

Metal-organic frameworks have been introduced as promising alternative materials to improve the performance of conventional desiccant wheels (DWs) in desiccant cooling systems. However, simultaneously achieving high dehumidification and energy performance of DWs coated with advanced desiccant materials is challenging owing to the trade-off between these indices. In this study, based on multi-objective optimization, the dehumidification and energy performance improvement of a DW coated with MIL-100 (Fe) (MCDW) over a conventional DW coated with silica gel (SGDW) and silica composite (SCDW) were evaluated. Based on the data measured in this study, a metamodel was developed using an artificial neural network to predict the performance of the MCDW. Furthermore, Morris sensitivity analysis was conducted to determine the decision variables. The optimal solutions were determined using multi-objective optimization and a Euclidean distance-based approach. Finally, a comparative performance evaluation between the optimized MCDW and SCDW was conducted under baseline conditions. As a result, the optimized MCDW showed higher dehumidification and energy performance than the optimized SCDW and SGDW.

Predicting and Controlling Ribbing Instabilities of Carbon Nanotube–PDMS Thin‐Film Systems for Multifunctional Applications

The manufacturing of thin films with structured surfaces by large‐scale rolling has distinct advantages over other techniques, such as lithography, due to scalability. However, it is not well understood or quantified how processing conditions can affect the microstructure at different physical scales. Hence, the objective of this investigation is to develop a validated computational model of the symmetric forward‐roll coating process to understand, predict, and control the morphology of carbon nanotube (CNT)–polydimethylsiloxane (PDMS) pastes. The effects of the thin‐film rheological properties and the roller gap on the ribbing behavior are investigated and a ribbing instability prediction model is formulated from experimental measurements and computational predictions. The CNT–PDMS thin‐film system is modeled by a nonlinear implicit dynamic finite‐element method that accounts for ribbing instabilities, large displacements, rolling contact, and material viscoelasticity. Dynamic mechanical analysis is used to obtain the viscoelastic properties of the CNT–PDMS paste for various CNT weight distributions. Furthermore, a Morris sensitivity analysis is conducted to obtain insights on the dominant predicted characteristics pertaining to the ribbing microstructure. Based on the sensitivity analysis, a critical ribbing aspect ratio is identified for the CNT–PDMS system corresponding to a critical roller gap.

Estimating hydrogeological parameters at groundwater level observation wells without pumping well information

Aquifer pumping is a common method of meeting human water needs, but it can also disrupt the natural flow of groundwater and cause fluctuations in groundwater levels. These fluctuations, however, can provide useful insights into hydrogeological properties when measured in an observation well. Traditional modeling methods are not well suited for analyzing such data because they lack essential details about the pumping wells, such as their number, distance from the observation well, and pumping rates. To address this issue, new impulse response function for transfer function modeling is developed that accurately describes the confined flow in such situations. The proposed function is based on the Theis solution, which uniformly distributes the effects of multiple pumping wells within a circular area around the observation well. This allows our function to accurately estimate hydrogeological parameters and pumping rates without knowing the number and specific location of pumping wells. The effects of well distance, spatial arrangement, and aquifer anisotropy on the accuracy of our function are also analyzed using synthetic experiments and Morris sensitivity analysis. The results show that the proposed function is effective in estimating essential hydrogeological parameters, such as the aquifer storativity, using only drawdown time series data from a observation well.

Ultimate bearing capacity prediction method and sensitivity analysis of PBL

The ultimate bearing capacity of Perfobond leiste (PBL) is one of the key parameters to evaluate the bearing capacity and reliability of steel–concrete structures, and it is very important to predict the ultimate bearing capacity of PBL more accurately. Based on the improved cuckoo search algorithm (CS), two prediction model optimized by back propagation neural network (BPNN) algorithm and extreme learning machine (ELM) were proposed. The local search ability of CS algorithm was improved by the triangular mutation operator and the distance-based distributed discovery probability, and the global search ability was improved by multi-step selection strategy. The weight, threshold, number of input parameters and number of nodes in the hidden layer of BPNN and ELM were optimized by the triangular multi-step cuckoo search (TMCS). The comprehensive sensitivity analysis (CSA) method and Morris sensitivity analysis (MSA) method were used to analyze the sensitivity of six key parameters of PBL, such as thickness of perforated steel plate, diameter of perforated holes, number of perforated holes, diameter of through reinforcement, yield strength of through reinforcement and compressive strength of concrete. The experimental data of push-out tests in published literatures were selected as samples, and the results show that the proposed TMCS-ELM algorithm and TMCS-BPNN algorithm can accurately predict the ultimate bearing capacity of PBL, and the average errors are 4.17% and 2.16% respectively. The sensitivity analysis results show that the compressive strength of concrete has the highest influence on the bearing capacity of PBL, followed by the yield strength of through reinforcement.

Spatiotemporal Evolution and Attribution Analysis of Water Yield in the Xiangjiang River Basin (XRB) Based on the InVEST Model

As a result of climate change and human activities, water resources in the Xiangjiang River Basin (XRB) are subject to seasonal and regional shortages. However, previous studies have lacked assessment of the spatiotemporal evolution of water yield in the XRB at seasonal and monthly scales and quantitative analysis of the driving forces of climate change and land use on water-yield change. Quantitative evaluation of water yield in the XRB is of great significance for optimizing water-resource planning and allocation and maintaining ecological balance in the basin. In this paper, the seasonal water-yield InVEST model and modified Morris sensitivity analysis were combined to study the characteristics of monthly water yield in the XRB. Seventeen attributes were identified using the Budyko framework. The results show that: (1) the water yield of the XRB showed an increase trend from northeast to southwest from 2006 to 2020; (2) the transfer-in of unused land, grassland, woodland and farmland as well as the transfer-out of water and construction land have positive effects on the increase in water yield, and the change to construction land has the greatest impact on water yield; (3) water yield is positively correlated with NDVI and precipitation and negatively correlated with potential evapotranspiration; (4) climate change and land-use change contributed to water-yield changes of 67.08% and 32.92%, respectively.

Multi-objective optimization of Venetian blinds in office buildings to reduce electricity consumption and improve visual and thermal comfort by NSGA-II

Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively.

Computational modelling and simulation to mitigate the risk of daylight exposure in tropical museum buildings

In museum design and operation, daylight is typically discouraged due to high risk of damaging the display objects. However, past studies in high-latitude regions have shown the possibility to apply daylight in museums, so long as it is carefully planned, and the display objects are not from the category of high responsive materials. In the tropical region, the influence of daylighting on light exposure on museum objects is still unknown. This study therefore aims to assess and mitigate the impact of annual daylight exposure on objects with low responsive materials in a tropical daylit museum building. Annual daylight modelling and simulation are performed to achieve the objective, followed with Morris sensitivity analysis and Mahalanobis distance classifier to optimise the outcome. It is found that either WWR or glazing transmissivity gives the greatest influence on the performance indicators. Based on the proposed optimisation algorithm, it is possible to determine the optimum solutions satisfying the performance indicators target, for a certain opening type. Overall, the contribution of this study is the proposed computational modelling and simulation methods to mitigate the exposure risk while optimising daylight as a renewable energy source.

Hygrothermal calibration and validation of vernacular dwellings: A genetic algorithm-based optimisation methodology

Heritage model calibration and validation are crucial for decreasing uncertainty and enhancing the robustness of simulation results and conservation interventions. Yet, hygrothermal modelling methodologies are marked by significant heterogeneity and lack of robustness. Aiming to provide a solution for the drawbacks identified, this study puts forth a comprehensive hygrothermal modelling methodology. Following the in situ data collection of a subset of heritage buildings, 22 vernacular dwellings in Southern Portugal, a three-step method was developed, consisting of: Morris sensitivity analysis, optimisation-based calibration, and validation and multi-criteria decision-making (MCDM). A genetic algorithm multi-objective optimisation-based calibration with NSGA-II was implemented for simultaneously minimising the statistical indicators RMSE and MAE for the indoor air temperature of the winter and summer models. The validation and MCDM were conducted by means of threshold compliance and Compromise Programming. NSGA-II found Pareto frontiers composed of nine and six optimal solutions for the summer and winter models, respectively, in nearly 3 h each. All optimal solutions significantly decreased the RMSE and MAE, especially in the summer model, regarding the baseline data. The final solutions selected after the MCDM resulted in an accuracy improvement of 51% and 54% for the RMSE and MAE for the winter model and 80% and 81% for the RMSE and MAE in the summer model, compared to the baseline models. The strong correlation found between the calibrated models and the measured data along with the enhancement of calibrated data regarding the baseline model, highlighted the potential of using GAs to obtain calibrated vernacular models that robustly predict real building performance and foster better retrofitting decision-making.

Coupled electromagnetic and mechanical modeling and detection of buried objects

A time domain finite element (FE) framework was used to investigate the coupled electromagnetic (EM) and mechanical behavior of buried target systems. The coupling of the EM and mechanical fields is through using the Lorentz Force as the body force in the mechanical Cauchy equation of motion. The coupling is sequential where the EM fields and Lorentz force are first solved for, then they are used as inputs for the Cauchy equations of motion. Predictions were obtained for different loading conditions and the Method of Morris sensitivity analysis was used to understand how different mechanical and EM variables affect the buried target system. These predictions indicate that target permeability and depth had the most significant and dominant effects on the behavior of the buried target system.

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification.

Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landmark-based detection and quantification of hip abnormalities from magnetic resonance (MR) images. The framework relies on a novel idea of multi-landmark environment analysis with reinforcement learning. In particular, we merge the concepts of the graphical lasso and Morris sensitivity analysis with deep neural networks to quantitatively estimate the contribution of individual landmark and landmark subgroup locations to the other landmark locations. Convolutional neural networks for image segmentation are utilized to propose the initial landmark locations, and landmark detection is then formulated as a reinforcement learning (RL) problem, where each landmark-agent can adjust its position by observing the local MR image neighborhood and the locations of the most-contributive landmarks. The framework was validated on T1-, T2- and proton density-weighted MR images of 260 patients with the aim to measure the lateral center-edge angle (LCEA), femoral neck-shaft angle (NSA), and the anterior and posterior acetabular sector angles (AASA and PASA) of the hip, and derive the quantitative abnormality metrics from these angles. The framework was successfully tested using the UNet and feature pyramid network (FPN) segmentation architectures for landmark proposal generation, and the deep Q-network (DeepQN), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and actor-critic policy gradient (A2C) RL networks for landmark position optimization. The resulting overall landmark detection error of 1.5 mm and angle measurement error of 1.4° indicates a superior performance in comparison to existing methods. Moreover, the automatically estimated abnormality labels were in 95% agreement with those generated by an expert radiologist.