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The study aims to analyse the stability of embankments over the improved ground with stone column (SC) and pervious concrete column (PCC) inclusions using limit equilibrium method. The short-term stability of PCC-supported embankment system is rarely addressed. Therefore, the factor of safety (FOS) of column-supported embankment system is calculated using individual column and equivalent area models.

The stability analysis of column-supported embankment system is conducted using PLAXIS LE 2D. The various geometrical and shear strength parameters influencing the FOS of these embankment systems such as diameter of columns, spacing between columns, embankment height, friction angle of column material, undrained cohesion of weak ground and cohesion of PCC are considered.

The critical failure envelope of PCC-supported embankment system is observed to be of toe failure, whereas the failure envelope of stone column-supported embankment system is generally of deep-seated nature.

It is found that for PCC embankment system, FOS and failure envelope are not influenced by the geometrical/shear strength parameters other than height of embankment. However, for stone column-supported embankment system, FOS and failure envelope are dependent on all the shear strength and geometrical parameters considered in this study.

Climate change has led to a rise in the frequency, intensity and scope of droughts, posing significant implications for businesses. This study examines the impact of local community drought levels on audit pricing. Additionally, it explores the moderating effects of high-tech industries, auditor busyness and the level of local community concern regarding the drought crisis.

This study employs a mixed-methods approach to rigorously test the research hypotheses. The quantitative phase of the study utilizes a sample of 1,278 firm-year observations from Iran’s capital market. For the analysis of the quantitative data, ordinary least squares regression with clustered robust standard errors is used. Additionally, this research supplements its quantitative findings with qualitative evidence obtained through semi-structured interviews with 19 Iranian audit partners.

The results suggest that firms operating in provinces facing severe droughts experience notably higher audit fees. Furthermore, the positive relationship between drought and audit fees is weakened when auditors are busy, local community concern regarding the drought crisis is high or the firm operates within high-tech industries. These findings are supported by a range of robustness checks and qualitative evidence gathered from the field.

This research contributes to the growing literature on climate change by examining the influence of local community drought levels on audit pricing within an Iranian context. Additionally, our study sheds light on how high-tech industries, auditor workload and the level of local community concern regarding the drought crisis moderate the relationship between drought and audit fees. Importantly, our study pioneers in providing mixed-methods evidence of the association between drought severity and audit fees.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.

The primary objective of this study is to produce one-way slabs made of LWFC with low density and sufficient compressive strength suitable for structural purpose then investigate their flexural behavior under various types of reinforcement and thickness of the slab and the influence of addition of PP fibers reinforcement on the mechanical behavior of reinforced concrete slabs. The specimens were tested using four-point loading. The results concerning load capacity, deflection and failure mode and crack pattern for each specimen were obtained. Also, an analytical investigation of PP fiber and GFG contribution on the flexural behavior of foamed concrete slabs is studied to investigate the significant role of PP fiber on the stress distribution in reinforced foam concrete and predict the flexural moment capacity.

The materials used in this study are cement, fine aggregate (sand), water, PP fibers, foaming agent, chemical additives if required, steel reinforcing rebars and glass fiber grid. The combination of these constituent materials will be used to produce foamed concrete in this research Then this study will present the experimental program of one-way foamed concrete slabs including slabs reinforced with GFR grids and another with steel reinforcements. The slabs will be tested in the laboratory under static loading conditions to investigate their ultimate capacities. The flexural behavior is to the interest of the slabs reinforced with GFR grids reinforcements in comparison with that of one with steel reinforcing rebars. Three groups are considered. (1) Group I: two slabs of PP fiber foamed concrete with minimum required reinforcements. (2) Group II: two slabs of PP fiber foamed concrete with glass fiber grids. (3) Group III: two slabs of PP fiber foamed concrete with the minimum required reinforcements and glass fiber grids.

The experimental results proved the effectiveness and efficiency of this the new system in producing a low density of concrete below 1900 kg/m³ had a corresponding strength of about 17 MPa at least. Besides, the presence of PP fibers had a noticeable improvement on the flexural strength values for all the examined slabs. It was found that the specimens reinforced with steel reinforcement mesh carried higher flexural capacity compared to these reinforced with GFG only. The specimens reinforced with GFG exhibited the lowest flexural capacity due to GFG separation from the concrete substrate. Also, an analytical investigation to predict the flexural strength of all tested specimens was carried out. The analytical results were agreed with the experimental results. Therefore, LWFC can be used as a substitute lightweight concrete material for the production of structural concrete applications in the construction industries today.

Foamed concrete is a wide field to discuss. To achieve the objectives of the project, the study is focused on the foamed concrete with the following limitations: (1) because the aim of this research is to produce foamed concrete suitable for structural purposes, it is decided to produce mixes within the density range 1300–1900 kg/m³. (2) Simply-supported slabs are of considered. (3) This study also looks out by using GFR and without it.

The main objectives of this study were producing structural foamed concrete slabs and investigate their flexural response for residential uses.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

This study aims to investigate the impact of board skill diversity (BSD) on corporate environmental responsibility (CER). In addition, this study explores the moderating effects of formal regulatory pressure and informal media pressure.

This study uses Chinese high polluting companies as the sample and uses regression analysis. Robustness checks, including instrumental variable regression, Heckman two-stage model and propensity score matching method, are performed to test the robustness of the results.

The findings suggest that BSD significantly improves CER performance. Both formal regulatory pressure and informal media pressure strengthen the positive impact of BSD on CER. Further channel analyses reveal that BSD improves CER performance by promoting corporate proenvironmental behaviors rather than by restricting environmental violations; skill diversity of executive directors has a more significant effect on CER than that of independent directors. Finally, the moderating effect of regulatory pressure is only significant after the implementation of the Environmental Protection Law, and the moderating effect of media pressure mainly concentrates on negative media coverage.

The involvement of directors with more diverse skills is essential to improve corporate proenvironmental behaviors. Companies should select qualified directors with different skills to further improve their performance on environmental protection and sustainable development.

Regulators and standard-setters should develop efficient guidelines on corporate board governance to enhance the positive role of companies in environmental and sustainable development.

This study broadens the research on the determinants of CER by examining the influence of BSD on CER and the moderating roles of various stakeholder pressures, thereby providing a deeper understanding of corporate environmental performance and sustainable development.

The purpose of this study is to use the corrected stress field theory to derive the shear capacity of geopolymer concrete beams (GPC) and consider the shear-span ratio as a major factor affecting the shear capacity. This research aims to provide guidance for studying the shear capacity of GPC and to observe how the failure modes of beams change with the variation of the shear-span ratio, thereby discovering underlying patterns.

Three test beams with shear span ratios of 1.5, 2.0 and 2.5 are investigated in this paper. For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities are 337kN, 235kN and 195kN, respectively. Transitioning from 1.5 to 2.0 results in a 30% decrease in capacity, a reduction of 102kN. Moving from 2.0 to 2.5 sees a 17% decrease, with a loss of 40KN in capacity. A shear capacity formula, derived from modified compression field theory and considering concrete shear strength, stirrups and aggregate interlocking force, was validated through finite element modeling. Additionally, models with shear ratios of 1 and 3 were created to observe crack propagation patterns.

For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities of 337KN, 235KN and 195KN are achieved, respectively. A reduction in capacity of 102KN occurs when transitioning from 1.5 to 2.0 and a decrease of 40KN is observed when moving from 2.0 to 2.5. The average test-to-theory ratio, at 1.015 with a variance of 0.001, demonstrates strong agreement. ABAQUS models beams with ratios ranging from 1.0 to 3.0, revealing crack trends indicative of reduced crack angles with higher ratios. The failure mode observed in the models aligns with experimental results.

This article provides a reference for the shear bearing capacity formula of geopolymer reinforced concrete (GRC) beams, addressing the limited research in this area. Additionally, an exponential model incorporating the shear-span ratio as a variable was employed to calculate the shear capacity, based on previous studies. Moreover, the analysis of shear capacity results integrated literature from prior research. By fitting previous experimental data to the proposed formula, the accuracy of this study's derived formula was further validated, with theoretical values aligning well with experimental results. Additionally, guidance is offered for utilizing ABAQUS in simulating the failure process of GRC beams.

This study aims to explore green practices within small and medium-sized enterprises (SMEs) and their implications for determining environmental performance. Targeting SMEs in Pakistan, the study examines the influence of green intellectual capital (GIC), innovation and creativity on environmental performance.

A comprehensive survey addressed top, middle, and lower-level managerial perspectives. A sample of 243 respondents was statistically selected, and the survey questionnaire was used to measure the key constructs of the study. Using a 5-point Likert scale, the study captured the respondents' insights regarding green practices. Data analysis was executed using SPSS for descriptive tests and Smart-PLS 4 for advanced structural equation modeling (SEM).

GIC significantly enhances green innovation within SMEs, leading to improved environmental performance. Green creativity is a crucial moderator, indicating that SMEs have higher creative approaches to counter environmental challenges. These findings accentuate the importance of fostering an environment that stimulates green creativity to uplift GIC in achieving environmental performance.

The study offers a profound understanding of how SMEs in Pakistan leverage GIC to elevate their environmental performance, thereby providing strategic insights for businesses aiming for sustainable growth.

In times of crisis, innovation management and specifically Research and Development (R&D) investments are critical to temper company losses and stimulate higher revenues. Environmental policies, for their potential to stimulate environmental innovations and efficient management of resources, may hold a magnifying role in this relationship. By relying on the distinction between regulatory policies and institutional incentives, this paper argues about the moderating role of environmental policies between a firm's R&D expenses and its performance.

Hypotheses are tested on data collected from a sample of small and medium-sized Chinese enterprises after the 2008 financial crisis.

Findings reveal positive moderating effects of both regulatory pressures and institutional incentives, with a more significant effect of government support. The highest impact is reached when both these types of policies are present.

The theoretical and methodological relevance of this distinction, the importance of an appropriate mix of environmental policies in policymaking and their resilience building role in stimulating environmental innovations in the aftermath of crises are discussed.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.