Empirical Predictive Model Research Articles

Mould Risk in Technical Room in Four Underground Water Reservoirs

Abstract This conference article analyses the indoor climate of a technical room in four underground water reservoirs. This indoor climate with minimal input energy is specific by high relative humidity and low air temperature, as shown in the experimental measurement. The unsteady 2D numerical simulation of the detail section (partly sub-terrain peripheral wall and floor) in software Calculation Area 4.0e calculates fluctuation of internal surface temperature and relative humidity. Finally, the empirical predictive Finnish VVT model predicts the seasonal mould risk on the internal surface of the peripheral wall.

An empirical predictive model for determining the aqueous solubility of BCS class IV drugs in amorphous solid dispersions

Context Determining solubility of drugs is laborious and time-consuming process that may not yield meaningful results. Amorphous solid dispersion (ASD) is a widely used solubility enhancement technique. Predictive models could streamline this process and accelerate the development of oral drugs with improved aqueous solubilities. Objective This study aimed to develop a predictive model to estimate the solubility of a compound from the ASDs in polymer matrices. Methods ASDs of model drugs (acetazolamide, chlorothiazide, furosemide, hydrochlorothiazide, sulfamethoxazole) with model polymers (PVP, PVPVA, HPMC E5, Soluplus) and a surfactant (TPGS) were prepared using hotmelt process. The prepared ASDs were characterized using DSC, FTIR, and XRD. The aqueous solubility of the model drugs was determined using shake-flask method. Multiple linear regression was used to develop a predictive model to determine aqueous solubility using the molecular descriptors of the drug and polymer as predictor variables. The model was validated using Leave-One-Out Cross-Validation. Results The ASDs’ drug components were identified as amorphous via DSC and XRD Studies. There were no significant chemical interactions between the model drugs and the polymers based on FTIR studies. The ASDs showed a significant (p < 0.05) improvement in solubility, ranging from a 3-fold to 118-fold, compared with the pure drug. The developed empirical model predicted the solubility of the model drugs from the ASDs containing model polymer matrices with an accuracy greater than 80%. Conclusion The developed empirical model demonstrated robustness and predicted the aqueous solubility of model drugs from the ASDs of model polymer matrices with an accuracy greater than 80%.

An Empirical Predictive Model for Formation Rate of the Day 5 Blastocyst

An Empirical Predictive Model for Formation Rate of the Day 5 Blastocyst

Empirical predictive model for seismic damage and economic losses of Italian school building heritage

Schools should be safe places where children and young people are hosted during their training years to develop their abilities and skills and to receive an education that makes them prepared to become the future of the nations where they live. Even though these aspects are of paramount importance and widespread recognized, the same importance does not seem to be equally ensured to school facilities. Educational institutes, indeed, are often old and realized without recent safety Standards, becoming consequently non-resilient, nor robust structures towards multiple natural hazards. This aspect concerns lots of countries worldwide, without making distinction among their richness level.In this paper empirical models for the prediction of damage and retrofit costs expected after earthquakes are presented. These probabilistic models relate seismic intensity to expected damage and economic losses, through the definition of continuous indexes, and are based on data collected after a recent earthquake occurred in Central Italy. Empirical data, required to define the probabilistic models, have been obtained by combining information coming from shaking maps, provided by the National Institute for Geophysics and Volcanology (INGV), information on the building properties (e.g. locations, dimensions, construction typology), provided by the Ministry of Education, and data about the damage suffered by constructions and funding for retrofit actions including damage repairing and seismic upgrading, provided by the Reconstruction Office. Proposed models provide the opportunity to directly derive fragility curves, to make it possible the use of the research outcomes in risk frameworks based on a discrete description of the damages and losses.Model parameters have been calibrated for the considered set of available data, but the methodology is general and can be also applied to different contexts, given data are available. Finally, the proposed models have been used to simulate the post-earthquake damage and losses scenario following a seismic event. A proposal for the analyses of probabilistic outputs have been developed to provide information useful to decision making purpose.

Observed damage and empirical predictive model of the school building heritage of the Marche region

The 2016 seismic sequence that struck Central Italy, with its three mainshocks and numerous aftershocks, caused widespread damages to building heritage. Among the buildings that suffered damages, schools are considered relevant structures from multiple points of view. Educational institutes, indeed, host children and young people during their training years in facilities that are often old and realized without recent safety Standards becoming, therefore, non-resilient, nor robust structures towards multiple natural hazards. In this paper, a database containing information regarding the geolocation of individual buildings, no. of floors, the floor surface, the construction period, the structural type and the potential presence of landscape or hydrogeological constraints, has been realized with reference to the schools of the Marche region, by merging different sources of information. In addition, the database also collects information regarding observed damages and recorded seismic intensities to schools during the 2016 Central Italy earthquake.Once built up the database, an empirical predictive model, known in literature and already used for the evaluation of potential post-earthquake scenarios to cultural heritage, is applied to school buildings located into the seismic crater of the Marche region, by estimating the parameters of the model through inference techniques. The probabilistic model applied, relates a measure of the ground motion intensity to an overall damage index, continuous in the range [0,1]. Moreover, if one integrates the partial information related to damaged buildings often provided by the post-earthquake surveys, with the data related to the undamaged and/or collapsed structures, the model can provide a full risk assessment procedure. Finally, fragility curves are also derived, to make it possible the use of the research outcomes in risk frameworks based on a discrete description of the consequences.

Kinetics of Ti3AlC2 Etching for Ti3C2Tx MXene Synthesis

The family of two-dimensional (2D) carbides and nitrides called MXenes has grown to encompass numerous structures and compositions. MXenes have been explored in a variety of applications such as energy storage, wireless communication, optoelectronics, and medicine because of their high electrical conductivity, redox-active surfaces, plasmonic behavior, and other attractive properties. Knowledge of the process kinetics is of fundamental importance for synthesis and property control of MXenes. Prediction of the optimal processing time as a function of various parameters will also facilitate scaling wet chemical synthesis of MXenes for industrial use. Herein, we performed a systematic study of the kinetics of the MAX phase precursor etching reaction for topochemical MXene synthesis by collecting and tracking the evolution of the byproduct H2 gas. For the Ti3AlC2 MAX to Ti3C2Tx MXene conversion, we investigated the influence of critical parameters, such as etchant composition, concentration, temperature, and MAX particle size, on the etching kinetics and developed an empirical predictive model to determine the optimal synthesis conditions given any input parameters. We tested a set of 12 kinetics models as well as a model-free fitting method and found the best agreement with the experimental results from three models and the model-free method (R2 > 0.990). The measured apparent activation energies ranged from 54.2 to 55.7 kJ/mol. Overall, our results suggest NH4HF2 as the most efficient etchant, such that the etching time required to produce Ti3C2Tx can be reduced to a few hours. We also demonstrated the importance of separating MAX powders based on particle size into narrow fractions. Finally, we discuss how this method can be improved and applied to study yet-to-be synthesized MXenes and how the MAX/MXene transformation can serve as a platform to model reactions under confinement.

Chloride-salinity as indicator of the chemical composition of groundwater: empirical predictive model based on aquifers in Southern Quebec, Canada.

The present study first describes the variations in concentrations of 12 chemical elements in groundwater relative to salinity levels in Southern Quebec (Canada) groundwater systems, and then uses this data to develop an empirical predictive model for evaluating groundwater chemical composition relative to salinity levels. Data is drawn from a large groundwater chemistry database containing 2608 samples. Eight salinity classes were established from lowest to highest chloride (Cl) concentrations. Graphical analyses were applied to describe variations in major, minor, and trace element concentrations relative to salinity levels. Results show that the major elements were found to be dominant in the lower salinity classes, whereas Cl becomes dominant at the highest salinity classes. For each of the major elements, a transitional state was identified between domination of the major elements and domination of Cl. This transition occurred at a different level of salinity for each of the major elements. Except for Si, the minor elements Ba, B, and Sr generally increase relative to the increase of Cl. The highest Mn concentrations were found to be associated with only the highest levels of Cl, whereas F was observed to be more abundant than Mn. Based on this analysis of the data, a correlation table was established between salinity level and concentrations of the chemical constituents. We thus propose a predictive empirical model, identifying a profile of the chemical composition of groundwater relative to salinity levels, to help homeowners and groundwater managers evaluate groundwater quality before resorting to laborious and costly laboratory analyses.

Empirical modelling of shear behavior of oil contaminated sand - geogrid interface

ABSTRACT Based on the experimental studies, a semi empirical predictive model has been presented and discussed in this paper to simulate the interface shear behavior of used diesel engine oil (UDEO) contaminated (0%, 2%, 4%, 6% and 8%) sand and geogrids. Experiments were conducted in a standard large-scale direct shear apparatus, which demonstrated that when the oil content increases from 0% to 8%, there is a drop in shear strength at the interface, with a marginal strain hardening trend. While the peak friction angle decreased as the UDEO content increased, the apparent cohesion showed an increasing trend. In addition to UDEO content, the normal stress is also an influencing parameter in the interface shear behavior. In order to simulate the interface shear behavior, a best-fit semi-empirical hyperbolic model has been suggested from the observed data using least square error method. The proposed model showed an excellent agreement with the experimentally observed behavior for both sand–sand and sand–geogrid interface.

Performance evaluation of recycled asphalt mixtures with various percentages of RAP from the rotary decomposition process

This paper conducted an experimental study to evaluate the performance of recycled asphalt mixtures containing various percentages of reclaimed asphalt pavement (RAP). The variabilities and properties of RAP materials from the rotary decomposition process were determined first, through which the RAP materials were mainly decomposed in three stages including reclaimed stone aggregates barely containing asphalt, and asphalt-rich mortar sand. The results demonstrated that the mineral grading and asphalt percentage of decomposition products were stable. Then, the design aggregate gradation of AC − 20F was selected and its optimum asphalt content (OAC) was obtained using the Marshall mix design method. In addition, the laboratory performance, including the high-temperature performance, low-temperature crack resistance, moisture susceptibility and dynamic modulus tests, of recycled asphalt mixtures with varying RAP contents was conducted. The test results indicated that when the RAP content increased, the rutting resistance improved, but the crack resistance at low temperature and moisture damage susceptibility worsened. The RAP content had an effect on the dynamic modulus, especially at a lower loading frequency (high temperature). And the dynamic modulus of recycled asphalt mixtures increased with the loading frequency increasing and temperature decreasing. The dynamic modulus of the recycled asphalt mixture under standard conditions (20 °C and 10 Hz) was determined to be 11500–13000 MPa. The recycled asphalt mixture with higher percentages of RAP (more than 40%) was applicable to be used as a high modulus asphalt mixture (HMAM) in the future based on the results of the dynamic modulus test. Finally, an empirical predictive model of the dynamic modulus for recycled asphalt mixtures was established.

Empirical Predictive Model for Seismic Damage and Economic Losses of Italian School Building Heritage

Empirical Predictive Model for Seismic Damage and Economic Losses of Italian School Building Heritage

Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation

In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.

A Newly Developed Empirical Predictive Model for the Dispersed Phase (DP) Holdup in Rotating Disc Contactors

Newly novel developed correlations were derived to predict the dispersed phase (DP) holdup in a rotating disc contactor (RDC) extraction column. DP holdup is one of the significant parameters in the design of liquid–liquid contactors and for calculating their production capacity. Despite the availability of quite a large number of holdup prediction correlations for the RDC, most of these correlations are either general in nature or valid for a limited range of operating conditions. This study conducted an experimental and theoretical investigation of the RDC holdup under the influence of varying geometries, including variations in the dispersed phase distributor, speed of the disc, flow rate, and physical characteristics of the system. The analysis revealed that the holdup decreased with an increasing distributor hole diameter and increased with an increasing disc speed and total flow rate. The effect of the physical properties on the holdup was larger than the effect of the disc speed. Using the measurements of over 150 runs, two RDC column holdup predictive models were proposed and evaluated. The first correlation was derived in terms of the distributor hole diameter, operating parameters, system physical properties, and column geometry. The second correlation excluded the column geometry. These correlations, which consider the distributor hole inlet diameter in predicting the DP holdup for an RDC column, were presented for the first time in this study. The predictive capability of these correlations was evaluated via their standard deviation (SD) and mean average percentage error (MAPE). The respective SD and MAPE of the two correlations were 1.7 and 5.2% for the first correlation and 1.6 and 11.4% for the second.

Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization.

Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset. This allowed an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils less than 6% OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils greater than 6% OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm-3, ~56% mean RMSE). Disaggregation by vegetation type, or region did not alter the breakpoint significantly (6% OC) nor improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered, harmonized CONUS wetland soil maps revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland, organic wetland soils, (35% lower than SSURGO-derived SOC stocks). Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0-30 cm and 30-100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). Future CONUS wetland soil C assessments will benefit from focused attention on improved organic wetland soil measurements, land history, and spatial representativeness.

Predictive quantitative model for assessing the asphalt-aggregate adhesion quality based on aggregate chemistry

Asphalt-aggregate adhesion quality is a key factor in the mechanical performance of asphalt pavements. This work presents a predictive model of moisture damage in asphalt-aggregate systems based on aggregate chemistry. The study included seven siliceous aggregates and two calcareous aggregates, and one source of asphalt binder. Specimens composed of an aggregate core, a 20 μm thick asphalt film and a metallic stub were tested via a pull-off debonding experimental setup after multiple water conditioning processes. The experimental results i.e. maximum load at failure, Fmax , work of fracture, Wf , and adhesive failure area at the surface of the aggregate, A % show that for siliceous aggregates, the SiO2 content increases moisture susceptibility while the opposite is seen for Fe2O3 and CaO, and that for calcareous aggregates, moisture susceptibility increases with their SiO2 content. These results were used to propose, for the first time, an empirical moisture damage predictive model for siliceous aggregates based on aggregate chemistry.

An empirical predictive model for the dynamic resilient modulus based on the static resilient modulus and California bearing ratio of cement- and lime-stabilised subgrade soils

This study uses two typical subgrade soil samples selected from a highway construction site in Jilin Province, China, to formulate an empirical predictive model for the dynamic resilient modulus (Ed ) based on the static resilient modulus (Es ) and the California bearing ratio (CBR) of cement- and lime-stabilised subgrade soils. Laboratory experiments of the dynamic and static resilient modulus and the CBR are conducted for varying moisture contents, compaction degrees, and number of freeze–thaw cycles. The results indicate that the moisture content, compaction degree, and number of freeze–thaw cycles have similar effects on the dynamic and static resilient modulus, and the CBR of stabilised soils. The results indicate that the Ed , Es , and the CBR are quadratically correlated with the moisture content, positively correlated with the compaction degree and negatively related to the number of freeze–thaw cycles. The proposed empirical predictive model for the dynamic resilient modulus is finally established based on the static resilient modulus and CBR. It indicates that the logarithmic model could characterise the relationship among the CBR, the Ed , and Es for cement- and lime-stabilised soils better.

Empirical predictive model for seismic damage of historical churches

Generally, strong seismic events cause serious damage to cultural heritage, especially in ancient churches characterized by a high vulnerability due to the intrinsic peculiarities of their structural systems which are not able to develop efficient resisting mechanisms. Starting from observed damage of churches and recorded ground motions, it is possible to define empirical response models. In this paper, an empirical predictive model for the seismic damage of churches is proposed. In particular, the damage is expressed by a continuous index and the seismic action is described by a scalar intensity measure. A sample of churches falling into a limited area hit by the Central Italy 2016 seismic sequence, which caused notable damage to the cultural heritage, has been chosen to calibrate the parameters of the proposed predictive model. The considered sample covers a wide range of damage and intensity levels, and includes all the churches of the area. The parameters of the model have been evaluated using the maximum likelihood estimation, considering a double step in the optimization procedure that combines a discrete with a continuous approach. Alternative proposals have been considered for the shape functions used in the numerical approximation and the results are discussed. Finally, the predictive model is applied to evaluate the damage scenario after a single seismic event and to illustrate the potential application of this risk analysis in decision-making processes. In particular, a deterministic event of magnitude 5.9 generated by the Senigallia fault has been considered and the consequences on historical churches reported in the national catalogue, are evaluated.

Investigation of Graphite Nozzle Erosion in Hybrid Rockets Using Oxygen/High-Density Polyethylene

A recently developed reconstruction technique is used to investigate graphite nozzle erosion in two scales of hybrid rocket motors, 30-N-thrust class and 2000-N-thrust class, using oxygen as the oxidizer and high-density polyethylene as the fuel. Thermocouple measurements taken from within the nozzles are used to estimate nozzle throat wall temperature. Forty-four static firing tests were conducted under varying experimental conditions to confirm the validity of the reconstruction technique results, to investigate the conditions at the onset of erosion, and to formulate an empirical predictive model of nozzle erosion rate. Results show that a single formula that treats the combustion gas as a single oxidizing agent for which heterogenous rate constants are functions of equivalence ratio can satisfactorily replicate the erosion rate of graphite by a combustion gas containing multiple oxidizing species. Furthermore, the chemical-kinetic-limited conditions of the onset of nozzle erosion are specified by a novel empirical correlation, which shows that erosion begins at lower temperature and pressure in oxidizer-rich combustion gas than in fuel-rich combustion gas.

Optimizing risk mitigation investment strategies for improving post-earthquake road network resilience

The road network is a major urban lifeline, and its post-earthquake resiliency is crucial to rapid and reliable rescue work. This study aims to optimize risk mitigation investment strategies for improving post-earthquake road network resilience. First, instead of theoretical assumptions on link failure probability, an empirical predictive model of debris obstruction of collapsed buildings was used to calculate link connectivity probability. Second, the Monte Carlo simulation method was employed to calculate post-earthquake road network accessibility based on the results of link connectivity reliability. Third, an optimal risk mitigation investment model was used in order to maximize post-earthquake network accessibility with different government budgets. A case study was analyzed by using the proposed method in an Shanghai historical area. Results may help local governments make better decisions of risk mitigation strategies in regard to earthquakes.

Empirical predictive model for generating synthetic non-stationary stochastic accelerogram of the Iranian plateau: including far- and near-field effects as well as mainshock and aftershock categorization

This work proposes comprehensive empirical predictive equations for generating stochastic synthetic 3-dimensional accelerograms for the Iranian plateau based on the existing database. First, the databank of Iranian accelerograms is collected, sorted, processed, declustered and categorized into the pulse-like and non-pulse-like data. To simulate the artificial accelerograms, a stochastic model capable of handling both the temporal and spectral non-stationarity of accelerograms is adopted. By implementing nonlinear curve fitting, parameters of the stochastic model are estimated. Then, the recorded events are categorized into eight distinct groups based on the existence of pulse-like components, and whether the recorded accelerogram is a mainshock or aftershock. Next, for each group, a Bayesian linear regression analysis is performed to obtain empirical predictive equations with probabilistic perspective. This proposed set of equations can estimate parameters of the stochastic model by using input seismological properties including event magnitude, focal depth, epicentral distance and average shear wave velocity of top 30 m of the subsurface profile. Finally, the validity of model is successfully assessed using the existing ground motion prediction equations for the Iranian plateau.

Empirical yield predictive models for the fisheries of irrigation reservoirs in Sri Lanka

As fisheries production in reservoirs of most countries is a secondary use, challenges for improved management of fisheries should be addressed by building partnership between fisheries and other interested groups such as agriculture concerned with water management. Attempts were therefore made to develop empirical fish yield predictive models in ten irrigation reservoirs of Sri Lanka incorporating morphological, edaphic and hydrological parameters together with fishing intensity, with a view to investigating their influence on fish yields. Reservoir fish yield was found to be significantly correlated with two formulations of morpho-edaphic index (i.e., conductivity in μS cm-1/mean depth in m [MEIc] and alkalinity in m. equiv. l-1)/mean depth in m [MEIa]), and a relative reservoir level fluctuation index (RRLF), defined as the mean amplitude of the annual reservoir level fluctuations divided by the mean depth of the reservoir. Both MEIc and MEIa also had significant positive ln-ln relationships with RRWL, indicating that RRWL can be used as an independent variable in reservoir fish yield prediction. Reservoir fish yield was also related to fishing intensity (FI in boat-days ha-1, yr-1) conforming to a ln-linear regression model (p Ln FY = 3.245 + 0.327 Ln MEIa + 0.023 FI (R2 = 0.355; p Ln FY = 3.403 + 0.249 Ln MEIc + 0.019 FI (R2 = 0.369; p Ln FY = 1.330 + 0.650 Ln RRWL + 0.016 FI (R2 = 0.593; p The empirical yield predictive model based on RRWL and FI as independent variables was more robust than those based on MEIa and MEIc, and the former has significant management implications because RRWL can be manipulated by irrigation authorities whereas control of FI is under the jurisdiction of fisheries authorities. Hence, through an effective dialogue between irrigation and fisheries authorities, there is a considerable potential to optimize fish yields in irrigation reservoirs of Sri Lanka.