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Increased competition and resource scarcity have caused hospitals to seek internal efficiencies by restructuring their structures and processes. The purpose of this paper is to examine the effects of an organization's orientation toward control and learning and the use of process facilitators on perceived organizational consensus on outcomes related to cost, quality, and the ability to sustain implemented changes following a major hospital restructuring.

Data from 263 hospitals from across the USA were collected. Factor analysis was employed to develop scales measuring the organization's emphasis on learning, controls, and processes. Regression analysis then examined their relationship to the consensus on restructured outcomes.

The findings suggest a positive relationship between a learning orientation and processes with improved perceived agreement on restructuring outcomes. Hospitals with control orientations have a negative relationship with perceived organizational consensus.

The research has some limitations. The primary data for both the CEOs' and employees' perspectives comes from hospital CEOs. Also, the study is a cross‐sectional study and lacks longitudinal information. It also includes mostly not‐for‐profit hospitals, with 100 or more beds, in urban areas.

Hospitals will continue to feel pressures for the need to restructure and change. The findings suggest that hospitals achieve better results if they foster a learning orientation and put in place processes to facilitate the challenges of change. Although control systems are important, executives should realize that they might impede organizational efforts during organizational change. Hospitals may succeed in their change efforts by balancing adequate control and learning that are supported by processes to facilitate restructuring efforts.

The work provides an original study on the effects of an organization's orientation of learning and controls and change processes on the perceived consensus of restructuring outcomes. The dichotomy of learning and controls has not been applied to hospital consensus on outcomes. The research suggests that hospitals can improve their change efforts by implementing appropriate processes and greater learning mechanisms. During times of stress and change hospitals often become more control oriented, which may create greater misalignments and ineffective change. Managers should learn from the research that appropriate processes and learning will provide better consensus and more effective change.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises SMEs through big data adoption (BDA).

The technology-organization-environment (TOE) framework was used as a theoretical base and data were gathered from manufacturing SMEs in Malaysia. The 159 questionnaire replies of chief executive officer (CEO)/managers were analyzed using a hybrid approach of structural equation modeling-artificial neural network (SEM-ANN).

The findings of this study showed that perceived benefits (PB), technological complexity (TC), organization's resources (OR), organization's management support (OMS) and government legislation (GL) are the factors that influence BDA and promote SM and SO. The findings of ANN showed that a perceived benefit is the most important factor, followed by OMS.

The findings of this study can assist SMEs managers in making strategic decisions and improving sustainable performance and thus contribute to overall economic development.

The manufacturing industry is under immense pressure to integrate sustainable practices for long-term success. BDA can assist industries in aligning industries' operational capabilities. The majority of the current research have mainly emphasized on BDA in corporations. However, the associations between BDA and sustainable performance of manufacturing SMEs have been less explored. To address this issue, this study developed a theoretical model and examined the influence of BDA on SM and SO of manufacturing SMEs. Meanwhile, the hybrid methodological approach can help to uncover both linear and non-linear relationships better.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength of green concrete. The proposed model allows the use of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash (FA) as partial replacements of concrete constituents.

The model is constructed, trained and validated using python through a set of experimental data collected from the literature. The model’s architecture comprises an input layer containing seven neurons representing concrete constituents and two neurons as the output layer to represent the 7- and 28-days compressive strength. The model showed high performance through multiple metrics, including mean squared error (MSE) of 2.41 and 2.00 for training and testing data sets, respectively.

Results showed that cement replacement with 10% FA causes a slight reduction up to 9% in the compressive strength, especially at early ages. Moreover, a decrease of nearly 40% in the 28-days compressive strength was noticed when replacing fine aggregate with 25% RFA.

The research is limited to normal compressive strength of green concrete with a range of 25 to 40 MPa.

The developed model is designed in a flexible and user-friendly manner to be able to contribute to the sustainable development of the construction industry by saving time, effort and cost consumed in the experimental testing of materials.

Green concrete containing wastes can solve several environmental problems, such as waste disposal problems, depletion of natural resources and energy consumption.

This research proposes a machine learning prediction model using the Python programming language to estimate the compressive strength of a green concrete mix that includes construction and demolition waste and FA. The ANN model is used to create three guidance charts through a parametric study to obtain the compressive strength of green concrete using RCA, RFA and FA replacements.

Because of the complexity of relationship between surface tension and its decisive factors, such as temperature, concentration, electronic density, molar atomic volume and electro-negativity, a reasonable predicting model of surface tension of Sn-based solder alloys has not been developed yet. The paper aims to address the surface tension issue that has to be considered if the new lead free solder will be applied for electronics.

Using an artificial neural network (ANN) model with back-propagation (BP) algorithm, the surface tension for Sn-based binary solder alloys was simulated, and the comparison between the simulating results and data from experiments and literatures was analyzed as well. In addition, the relationship between surface tension and its decisive factors would be discussed based on the ANN and orthogonal design methods.

It is shown that the predicting model of surface tension of Sn-based solder alloys is constructed according to the BP–ANN theory, and the predicted value from the BP–ANN is in excellent agreement with the experimental results. The surface tension of Sn-based solders is determined by five factors, i.e. temperature, concentration, electronic density, molar atomic volume and electro-negativity. Among of the factors, molar atomic volume is major factor, and the order of degree of influence on surface tension is molar atomic volume > electro-negativity > electronic > density > concentration > temperature. Moreover, a simply reasonable equation is proposed to estimate the surface tension for Sn-based solders.

The five decisive factors of surface tension for Sn-based binary solder alloys have been analyzed theoretically, and a reasonable model of surface tension for Sn-based binary solder alloys is proposed as well. It is shown that ANN theory will be applied well to simulate the surface tension of Sn-based lead free solder.