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Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

By inspecting the special connection between negligence and the causes of negligence, this study evaluates the reasonableness of negligence offenses elements from the perspectives of “modes” and “rules.” This study considers that in the core concept of negligent manslaughter in the Criminal Code of Taiwan, “business” comprises an outstretched legal element; thus, “application by analogy” or “customary laws” should be prohibited as a legal basis or when applying the criminal code because those are not allowed under nulla poena sine lege. Nulla poena sine lege must be respected to release construction professionals from material risks in their judicial rights and interests.

This study used data mining analysis with a database of 204 cases where construction professionals were involved in an accusation of gross negligence manslaughter (GNM) (N = 486) between 1995 and 2021 to explore the reasons and distribution of these cases in the construction industry in Taiwan.

The results showed that the main reasons behind lawful GNM accusations against construction professionals are as follows: (1) the violation of employers' duty of care to prevent hazards caused in workplaces where falling and collapsing are concerns during construction, thus resulting in death; (2) gross negligence during design, construction and supervision, causing damages after natural disasters such as earthquakes and typhoons.

This study discusses the whole life circle of construction, starting from planning, design, construction and completion. However, the involvement of other offenses such as providing false statements, forgery, embezzlement, unjust enrichment and fraudulent tax evasion or criminal responsibilities stipulated in the Building Act or administrative punishments are beyond the scope of this study. Future studies will focus on foreign “business GNM” cases from judicial precedents with similar backgrounds to Taiwan in the construction industry to verify whether similar conclusions can be drawn and to examine their differences.

This study applied data mining and data analysis to the data and explored potential causality and patterns of GNM cases in judicial cases. The results of the analyses can be used as evidence for potential causality and thus facilitate construction professionals' self-reflection and contribute to the sustainable development of working environments for construction.

This study agrees with the removal of GNM titled “business” in the Criminal Code of Taiwan to achieve Sustainable Development Goals (SDGs) in the building industry. By doing so, national judicial and management systems will be in line with international standards, ensuring that everyone has equal access to justice.

Goal 16 of the SDGs by the United Nations aims to promote judicial equality, peace, justice and strong institutions. With this basis, this study collected and analyzed data in the field of criminal law and applied the theory of criminal offenses committed by negligence to real construction-related cases. This study especially discusses whether construction professionals were imposed with excessive responsibilities when a court enforced the “duty of care” that asked the professionals to bear the responsibility of results for events that should be and could be foreseen.

This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.

A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.

The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.

Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.

The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.

The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.

In an era where complex technological advances increasingly govern service delivery, it is incumbent on service firms to pioneer innovative strategies to sustain customer engagement and cultivate loyalty. This conceptual paper examines the transformative potential of artificial intelligence (AI) in the realm of online customer communities, with a particular focus on its creation, management and enhancement facets. The authors explore how AI can revolutionize the dynamics of customer interaction, feedback mechanisms and overall engagement within the service industry.

This conceptual paper draws from marketing and management literature focusing on customer communities and AI in service and customer engagement contexts with a robust future research agenda.

A classification of online customer community engagement is provided along with a conceptual framework to guide our understanding of the integration of AI into online customer communities.

This exploration underscores the imperative for service firms to embrace AI-driven approaches to online customer community management, not only as a means to optimize their operations but as a vital strategy to stay competitive in the ever-evolving digital landscape. This paper examines the novel combination of AI with online customer communities and provides the framework in the form of an input-process-output (IPO) model for future research into this integration.

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.

The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.

The purpose of this paper is to study the corrosion behavior of Q235 steel in saturated acidic red and yellow soils.

The corrosion behavior of Q235 steel in saturated red and yellow soils was compared by weight-loss, SEM/EDS, 3D ultra-depth microscopy and electrochemical measurements.

R_p of the steel gradually increases and i_corr gradually decreases in both the red and yellow soils with time. The R_p of the steel in the red soil is lower, but its i_corr is higher than that in the yellow soil. The uniform corrosion rate, diameter and density of the corrosion pit on the steel surface in the red soil are greater than those in the yellow soil. Lower pH, higher contents of corrosive anions and high-valence Fe oxides in the red soil are responsible for its higher corrosion rates and local corrosion susceptibility.

This paper investigates the difference in corrosion behavior of carbon steel in saturated acidic red and yellow soils, which can help to understand the mechanism of soil corrosion.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

Amidst compounding crises and increasing global population’s nutritional needs, food supply chains are called to address the “diet–environment–health” trilemma in a sustainable and resilient manner. However, food system stakeholders are reluctant to act upon established protein sources such as meat to avoid potential public and industry-driven repercussions. To this effect, this study aims to understand the meat supply chain (SC) through systems thinking and propose innovative interventions to break this “cycle of inertia”.

This research uses an interdisciplinary approach to investigate the meat supply network system. Data was gathered through a critical literature synthesis, domain-expert interviews and a focus group engagement to understand the system’s underlying structure and inspire innovative interventions for sustainability.

The analysis revealed that six main sub-systems dictate the “cycle of inertia” in the meat food SC system, namely: (i) cultural, (ii) social, (iii) institutional, (iv) economic, (v) value chain and (vi) environmental. The Internet of Things and innovative strategies help promote sustainability and resilience across all the sub-systems.

The study findings demystify the structure of the meat food SC system and unveil the root causes of the “cycle of inertia” to suggest pertinent, innovative intervention strategies.

This research contributes to the SC management field by capitalising on interdisciplinary scientific evidence to address a food system challenge with significant socioeconomic and environmental implications.

The purpose of this study is to investigate starch mucor (SM) in potassium iodide (KI) as corrosion inhibitor of aluminium in hydrochloric acid (HCl) medium.

The SM in KI was characterized by gravimetric, scanning electron microscopy, electrochemical impedance spectroscopy measurements, potentiodynamic polarization and gas chromatography-mass spectrometer techniques. The inhibition efficiency was optimized using response surface methodology.

The result revealed that the inhibitor inhibited corrosion at a low concentration with the rate of inhibition increasing as the concentration of the inhibitor increased. The inhibition efficiency increases as the temperature was increased with slight incorporation of the inhibitor (SM in KI). This indicates that the corrosion control is both inhibitor (SM in KI) and temperature dependent.

The research results can provide the basis for using SM in KI as corrosion inhibitor of aluminium in HCL medium. Mixed-type inhibitor nature of SM was proved by cathodic and anodic nature of the polarization curves.