Search results

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

This research endeavors to assess the influence of financial shared service centers (FSSCs) on the quality of accounting information within China’s A-share listed companies. Using a multi-period difference-in-differences (DID) model, the study aims to empirically examine the correlation between the adoption of FSSCs and the quality of accounting information.

The study uses a robust methodology to evaluate the relationship between FSSCs and accounting information quality (AIQ). Leveraging the established FSSCs within China’s A-share listed companies as the treatment group, this research adopts a multi-period DID model. This approach enables a rigorous empirical examination of the influence exerted by FSSCs on the overall quality of accounting information.

The present study delves into the impact of FSSCs on AIQ and conducts empirical analysis using data from Chinese A-share listed companies between 2004 and 2021. The findings substantiate that: FSSCs significantly bolster the quality of accounting information, a conclusion retained even after robustness tests. Specifically, FSSCs exhibit a positive correlation with the comparability, timeliness and disclosure quality of accounting information while demonstrating no significant influence on relevance, robustness and reliability factors.

First, the analysis primarily rests upon data from Chinese A-share listed companies between 2004 and 2021, potentially constraining the generalizability of findings across diverse contexts. Second, despite controlling for various factors, unobserved variables or external factors not encompassed in the model might influence the relationship between FSSCs and AIQ. Additionally, the study’s reliance solely on quantitative data confines exploration into qualitative aspects that might offer a more comprehensive understanding of FSSCs’ impact on AIQ.

This paper establishes a nuanced connection between FSSC operations and AIQ, furnishing direct empirical evidence for their economic implications and propounding a novel avenue for augmenting AIQ. And, it furnishes guidance for forthcoming FSSC development, accentuating the necessity of harnessing information technology to enhance the relevance, reliability and robustness of accounting information.

Majority of prior empirical studies assessing AIQ have focused on singular indicators, lacking a comprehensive depiction of its overall level. To address this gap, this paper pioneers the construction of a comprehensive index for AIQ, providing a holistic representation of its level. Furthermore, this study stands as the inaugural investigation into the relationship between China’s A-share listed firms’ FSSCs and the quality of accounting information.