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This research proposes a viable method of slab and shore load computation for the partial striking technique utilized in high-rise construction projects to optimize the use of horizontal formwork. The proposed Partial Striking Simplified Method (PSSM) is designed to be utilized by industry practitioners to schedule the construction operations of casting floors in order to control the formwork costs incurred throughout the completion of a project.

The article presents the PSSM for calculating slab and shore loads in multi-story building construction. It introduces the concept of “clearing before striking,” where shore supports are partially removed after a few days of pouring fresh concrete. The PSSM procedure is validated through numerical analysis and compared to other simplified approaches. Additionally, a user-friendly Python program based on the PSSM procedure is developed to explore the capability of the PSSM procedure and is used to study the variations in slab load, shoring level, concrete grade and cycle time.

The study successfully developed a more efficient and reliable method for estimating the loads on shores and slabs using partial striking techniques for multi-story building construction. Compared to other simplified approaches, the PSSM procedure is simpler and more precise, as demonstrated through numerical analysis. The mean of shore and slab load ratios are 1.08 and 1.07, respectively, which seems to have a slight standard deviation of 0.29 and 0.21 with 3D numerical analysis. The Python program developed for load estimation is effective in exploring the capability of the proposed PSSM procedure. The Python program's ability to identify the floor under maximum load and determine the specific construction stage provides valuable insights for multi-story construction, enabling informed decision-making and optimization of construction methods.

High-rise construction in Indian cities is booming, though this trend is not shared by all the country's major metropolitan areas. The growing construction sector in urban cities demands rapid construction for efficient utilization of formwork to control the construction costs of project. The proposed procedure is the best option to optimize the formwork construction cost, construction cycle time, the suitable formwork system with optimum cost, concrete grade for the adopted level of shoring in partaking and many more.

The proposed PSSM reduces the calculation complexity of the existing simplified method. This is done by considering the identical slab stiffness and identical shore layout for uniform load distribution throughout the structure. This procedure utilizes a two-step load distribution calculation for clearing phase. Initially, the 66% prop load of highest floor level is distributed uniformly over the lower interconnected slabs. In the second step, the total prop load is removed equally from all slabs below it. This makes the load distribution user-friendly for the industry expert.

Python codes are developed for the versatile structural analysis on a 3 spar multi-cell box beam by means of idealization approach.

Shear flow distribution, stiffener loads, location of shear center and location of geometric center are computed via numpy module. Data visualization is performed by using Matplotlib module.

Python scripts are developed for the structural analysis of multi-cell box beams in lieu of long hand solutions. In-house developed python codes are made available to be used with finite element analysis for verification purposes.

The use of python scripts for the structural analysis provides prompt visualization, especially once dimensional variations are concerned in the frame of aircraft structural design. The developed python scripts would serve as a practical tool that is widely applicable to various multi-cell wing boxes for stiffness purposes. This would be further extended to the structural integrity problems to cover the effect of gaps and/or cut-outs in shear flow distribution in box-beams.

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

The purpose of this paper is to present the probabilistic approach to a new robustness-based design strategy for thin-walled composite structures in post-buckling.

Because inherent uncertainties in geometry, material properties, ply orientation and thickness affect the structural performance and robustness, these variations are taken into account.

The methodology is demonstrated for the sake of simplicity with an unstiffened composite plate under compressive loading, and the probabilistic and deterministic results are compared. In this context, the structural energy and uncertainties are employed to investigate the robustness and reliability of thin-walled composite structures in post-buckling.

As practical implication, the methodology can be extended to stiffened shells, widely used in aerospace design with the aim to satisfy weight, strength and robustness requirements. Moreover, a new argument is strengthened to accept the collapse close to ultimate load once robustness is ensured with a required reliability.

This innovative strategy embedded in a probabilistic framework might lead to a different design selection when compared to a deterministic approach, or an approach that only accounts for the ultimate load. Moreover, robustness measures are redefined in the context of a probabilistic design.

The purpose of the paper is to conduct a numerical and experimental investigation into the properties of nanofluids containing spherical nanoparticles of random sizes flowing through a porous medium. The study aims to understand how the thermophysical properties of the nanofluid are affected by factors such as nanoparticle volume fraction, permeability of the porous medium, and pore size. The paper provides insights into the behavior of nanofluids in complex environments and explores the impact of varying conditions on key properties such as thermal conductivity, density, viscosity, and specific heat. Ultimately, the research contributes to the broader understanding of nanofluid dynamics and has potential implications for engineering and industrial applications in porous media.

This paper investigates nanofluids with spherical nanoparticles in a porous medium, exploring thermal conductivity, density, specific heat, and dynamic viscosity. Studying three compositions, the analysis employs the classical Maxwell model and Koo and Kleinstreuer’s approach for thermal conductivity, considering particle shape and temperature effects. Density and specific heat are defined based on mass and volume ratios. Dynamic viscosity models, including Brinkman’s and Gherasim et al.'s, are discussed. Numerical simulations, implemented in Python using the Langevin model, yield results processed in Origin Pro. This research enhances understanding of nanofluid behavior, contributing valuable insights to porous media applications.

This study involves a numerical examination of nanofluid properties, featuring spherical nanoparticles of varying sizes suspended in a base fluid with known density, flowing through a porous medium. Experimental findings reveal a notable increase in thermal conductivity, density, and viscosity as the volume fraction of particles rises. Conversely, specific heat experiences a decrease with higher particle volume concentration.xD; xA; The influence of permeability and pore size on particle volume fraction variation is a key focus. Interestingly, while the permeability of the medium has a significant effect, it is observed that it increases with permeability. This underscores the role of the medium’s nature in altering the thermophysical properties of nanofluids.

This paper presents a novel numerical study on nanofluids with randomly sized spherical nanoparticles flowing in a porous medium. It explores the impact of porous medium properties on nanofluid thermophysical characteristics, emphasizing the significance of permeability and pore size. The inclusion of random nanoparticle sizes adds practical relevance. Contrasting trends are observed, where thermal conductivity, density, and viscosity increase with particle volume fraction, while specific heat decreases. These findings offer valuable insights for engineering applications, providing a deeper understanding of nanofluid behavior in porous environments and guiding the design of efficient systems in various industrial contexts.

The purpose of this paper is to present some of the key achievements. At DLR, a sophisticated interdisciplinary aircraft design process is being developed, using the CPACS data format (Nagel et al., 2012; Scherer and Kohlgrüber, 2016) as a means of exchanging results. Within this process, TRAFUMO (Scherer et al., 2013) (transport aircraft fuselage model), built on ANSYS and the Python programming language, is the current tool for automatic generation and subsequent sizing of global finite element fuselage models. Recently, much effort has gone into improving the tool performance and opening up the modeling chain to further finite element solvers.

Much functionality has been shifted from specific routines in ANSYS to Python, including the automatic creation of global finite element models based on geometric and structural data from CPACS and the conversion of models between different finite element codes. Furthermore, a new method for modeling and interrogating geometries from CPACS using B-spline surfaces has been introduced.

Several new modules have been implemented independently with a well-defined central data format in place for storing and exchanging information, resulting in a highly extensible framework for working with finite element data. The new geometry description proves to be highly efficient while also improving the geometric accuracy.

The newly implemented modules provide the groundwork for a new all-Python model generation chain, which is more flexible at significantly improved runtimes. With the analysis being part of a larger multidisciplinary design optimization process, this enables exploration of much larger design spaces within a given timeframe.

In the presented paper, key features of the newly developed model generation chain are introduced. They enable the quick generation of global finite element models from CPACS for arbitrary solvers for the first time.

The purpose of this sudy is to provide a complete optimization-based methodology to design waveguides with metamaterial walls. The present methodology is based on optimization. Indeed, the inverse problems of design are formulated as nonlinear black-box optimization problems with constraints. Two inequality black-box constraints are taken into account as penalized terms that are added to the objective function when the constraints are not satisfied. The numerical steps are done by using a finite element method solver (GetDP). Thus, different optimization software are tested to solve the nonlinear black-box optimization problems such as IPOPT, NLOPT and NOMAD from the Opti ToolBox in MatLab.

In this work, a methodology to design waveguides with metamaterial walls is proposed. The aim is to solve an inverse problem to find the best design where the electric field cartography is the closest to an imposed one.

The present methodology is applied to solve inverse problems of design and satisfactory results were provided by the three solvers IPOPT, NLOPT and NOMAD. Those numerical experiments show that NOMAD is the most efficient method to optimize the design of those cylindrical waveguide structures with metamaterial walls.

The model is set to find solutions using a specific pattern of metamaterials. This is promising to take those geometries as variables of the optimization problems. Moreover, in this exploratory work, no constraint on the fabrication limits has been taken into account.

The originality is to formulate design problems of waveguide with metamaterial walls into optimization problems. These optimization problems are difficult to solve because the objective function and two inequality constraints are computed via a numerical simulation code based on finite element methods. Thus, an original approach based on penalization is implemented and three optimization software are used. Hence, the authors propose an optimization-based methodology and apply to solve two inverse problems of design.

This paper aims to present a part of a coupled numerical model for prediction the fire resistance of elements in a horizontal furnace. Temperatures calculated inside the timber beam are compared to measured values from the fire test.

The paper presents a part of a coupled numerical model for prediction the fire resistance of elements in a horizontal furnace. The presented part lies in a virtual furnace which simulates temperature environment around tested elements in the furnace. Comparison of results show good agreement in the case when burning of timber is included in the numerical model.

The virtual furnace presented in this paper allows to calculate temperature environment around three timber beams. After validation of the fire dynamics simulator (FDS) model, the temperature conditions are passed to the FE model which solves heat transfer to the tested element. Temperatures inside the timber beam which are solved in software Atena Science are compared to measured temperatures from the fire test. The comparison of temperatures in three control points shows good accuracy of the calculation in the point closer to the heated edge. An inaccuracy is shown in points located deeper in the beam cross-section – below the char layer.

In conclusion, the virtual furnace has a great potential for investigating the thermal behaviour of fire-resistance tests. A huge advantage inheres in the evaluation of the thermal effect throughout the volume of the furnace, which allows an accurate prediction of fire-resistance tests and evaluation of large number of technical alternatives and boundary conditions. However, passing the temperature field from the FDS model into FE model may decrease the level of accuracy. The solution lies in a coupled CFD-FE model. A weakly coupled model including fluid dynamics, heat transfer and mechanical behaviour is under development at Faculty of Civil Engineering, Czech Technical University in Prague. The fluid dynamics part which is presented in this paper is solved by FDS and the thermo-mechanical part is computed by object-oriented finite element model (OOFEM). The interconnection of both software is made owing to MuPIF python library.

The virtual furnace takes advantage of great possibilities of computational fluid dynamics code FDS. The model is based on an accurate representation of a real fire furnace of fire laboratory PAVUS a.s. located in the Czech Republic. It includes geometry of the real furnace, material properties of the furnace linings, burners, ventilation conditions and tested elements. Gas temperature calculated in the virtual furnace is validated to temperatures measured during a fire test.

The virtual furnace has a great potential for investigating the thermal behaviour of fire-resistance tests. A huge advantage inheres in the evaluation of the thermal effect throughout the volume of the furnace, which allows an accurate prediction of fire-resistance tests and evaluation of large number of technical alternatives and boundary conditions.

The virtual furnace has a great potential for investigating the thermal behaviour of fire-resistance tests. A huge advantage inheres in the evaluation of the thermal effect throughout the volume of the furnace, which allows an accurate prediction of fire-resistance tests and evaluation of large number of technical alternatives and boundary conditions. However, passing the temperature field from the FDS model into FE model may decrease the level of accuracy. The solution lies in a coupled CFD-FE model. A weakly coupled model including fluid dynamics, heat transfer and mechanical behaviour is under development at Faculty of Civil Engineering, Czech Technical University in Prague. The fluid dynamics part which is presented in this paper is solved by FDS and the thermo-mechanical part is computed by OOFEM. The interconnection of both software is made thanks to MuPIF python library.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The purpose of this paper is to propose a numerical prediction method of buckling loads for shell structures under axial compression and thermal loads based on vibration correlation technique (VCT).

VCT is a non-destructive test method, and the numerical realization of its experimental process can become a promising buckling load prediction method, namely numerical VCT (NVCT). First, the derivation of the VCT formula for thin-walled structures under combined axial compression and thermal loads is presented. Then, on the basis of typical NVCT, an adaptive step-size NVCT (AS-NVCT) calculation scheme based on an adaptive increment control strategy is proposed. Finally, according to the independence of repeated frequency analysis, a concurrent computing framework of AS-NVCT is established to improve efficiency.

Four analytical examples and one optimization example for imperfect conical-cylindrical shells are carried out. The buckling prediction results for AS-NVCT agree well with the test results, and the efficiency is significantly higher than that of typical numerical buckling methods.

The derivation of the VCT formula for thin-walled shells provides a theoretical basis for NVCT. The adaptive incremental control strategy realizes the adaptive adjustment of the loading step size and the maximum applied load of NVCT with Python script, thus establishing AS-NVCT.