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Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Business process modeling faces a difficult balance: on the one hand, organizations seek to enact, control and automate business processes through formal structures (procedures and rules). On the other hand, organizations also seek to embrace flexibility, change, innovation, value orientation, and dynamic capabilities, which require informal structures (unique user experiences). Addressing this difficulty, the authors propose the composite approach, which integrates formal and informal process structures. The composite approach adopts a socio-material conceptual lens, where both material and human agencies are supported.

The study follows a design science research methodology. An innovative artifact – the composite approach – is introduced. The composite approach is evaluated in an empirical experiment.

The experimental results show that the composite approach improves model understandability and situation understandability.

This research explores the challenges and opportunities brought by adopting a socio-material conceptual lens to represent business processes.

The study contributes an innovative hybrid approach for modeling business processes, articulating coordination and contextual knowledge. The proposed approach can be used to improve model understandability and situation understandability. The study also extends the socio-material conceptual lens over process modeling with a theoretical framework integrating coordination and contextual knowledge.

The purpose of this paper is mainly to know: (1) the sound absorption coefficient of porous composite structures constituted by a new kind of lightweight ceramic foam and perforated plate; (2) the availability of an equivalent porous material model, recently proposed by the present author, to these composite structures in sound absorption.

A kind of lightweight ceramic foam with bulk density of 0.38–0.56 g·cm-3 was produced by means of molding, drying and sintering. The effect of stainless steel perforated plate on sound absorption performance of the ceramic foam was investigated by means of JTZB absorption tester.

The results indicate that the sound absorption performance could be obviously changed by adding the stainless steel perforated plate in front of the porous samples and the air gap in back of the porous samples. Adding the perforated plate to the porous sample with a relatively large pore size, the sound absorption performance could be evidently improved for the composite structure. When the air gap is added to the composite structure, the first absorption peak shifts to the lower frequency, and the sound absorption coefficient could increase in the low frequency range.

Based on the equivalent porous material model and the “perforated plate with air gap” model, the sound absorption performance of the composite structures can be simulated conveniently to a great extent by using Johnson-Champoux-Allard model.

The purpose of this paper is to establish a maturity evaluation model for the application of construction steel structure welding robotics suitable for the actual situation and specific characteristics of engineering projects in China and then to assess the maturity level of the technology in the application of domestic engineering projects more scientifically.

The research follows a qualitative and quantitative analysis method. In the first stage, the structure of the maturity model is constructed and the evaluation index system is designed by using the ideas of the capability maturity model and WSR methodology for reference. In the second stage, the design of the evaluation process and the selection of evaluation methods (analytic hierarchy process method, multi-level gray comprehensive evaluation method). In the third stage, the data are collected and organized (preparation of questionnaires, distribution of questionnaires, questionnaire collection). In the fourth stage, the established maturity evaluation model is used to analyze the data.

The evaluation model established by using multi-level gray theory can effectively transform various complex indicators into an intuitive maturity level or score status. The conclusion shows that the application maturity of building steel structure welding robot technology in this project is at the development level as a whole. The maturity levels of “WuLi – ShiLi – RenLi” are respectively: development level, development level, between starting level and development level. Comparison of maturity evaluation values of five important factors (from high to low): environmental factors, technical factors, management factors, benefit factors, personnel and group factors.

In this paper, based on the existing research related to construction steel structure welding robot technology, a quantitative and holistic evaluation of the application of construction steel structure welding robot technology in domestic engineering projects is conducted for the first time from a project perspective by designing a maturity evaluation index system and establishing a maturity evaluation model. This research will help the project team to evaluate the application level (maturity) of the welding robot in the actual project, identify the shortcomings and defects of the application of this technology, then improve the weak links pertinently, and finally realize the gradual improvement of the overall application level of welding robot technology for building steel structure.

Reliable estimations of the extent of corrosion and time required to reach specific safety limits are crucial for assessing the reliability of aging reinforced concrete (RC) bridges. Engineers and decision-makers can use these figures to plan suitable inspection and maintenance operations.

Analytical, empirical and numerical approaches for estimating the service life of corroded RC structures were presented and compared. The concrete cover cracking times, which were predicted by the previously proposed analytical models, were compared with the experimentally obtained cracking times to identify the model/s for RC bridges. The shortcomings and limitations of the existing models are discussed.

The empirical models typically depend on the rate of corrosion, diameter of steel reinforcement and concrete cover depth and based on basic mathematical formula. In contrast, the analytical and numerical models contain the strength and stiffness properties of concrete as well as type of corrosion products and incorporate more complex mechanical factors. Four existing analytical models were analyzed and their performance was evaluated against existing experimental data in literature. All the considered analytical models were assumed thick-walled cylinder models. The maximum difference between observed cracking time from different test data and calculated cracking time using the developed models is 36.5%. The cracking times extend with increase in concrete cover and decrease with corrosion current density. The development of service life prediction models that considers factors such as heterogeneity of concrete, non-uniform corrosion along rebar, rust production rate and a more accurate representation of the corrosion accommodating region are some of the areas for further research.

Outcome of this paper partially bridge the gap between theory and practice, as it is the basis to estimate the serviceability of corrosion-affected RC structures and to propose maintenance and repair strategies for the structures. For structural design and evaluation, the crack-width criterion is the greatest practical importance, and structural engineers, operators and asset managers should pay close attention to it. Additionally, repair costs for corrosion-induced serviceability failures, particularly concrete cracking and spalling, are significantly higher than those for strength failures. Therefore, to optimize the maintenance cost of RC structures, it is essential to precisely forecast the serviceability of corrosion-affected concrete structures. The lifespan of RC structures may be extended by timely repairs. This helps stake holders to manage the resources.

In order to improve modeling of corrosion-induced cracking, important areas for future research were identified. Heterogeneity properties of concrete, concept of porous zone (accommodation effect of pores should be quantified), actual corrosion morphology (non-uniform corrosion along the length of rebar), interaction between sustain load and corrosions were not considered in existing models. Therefore, this work suggested for further researches should consider them as input and develop models which have best prediction capacity.

This work has positive impact on society and will not affect the quality of life. Predicting service life of structures is necessary for maintenance and repair strategy plans. Optimizing maintenance strategy is used to extend asset life, reduce asset failures, minimize repair cost, and improve health and safety for society.

The degree of accuracy and applicability of the existing service life prediction models used for RC were assessed by comparing the predicted cracking times with the experimentally obtained times reported in the literature. The shortcomings of the models were identified and areas where further research is required are recommended.

Flexibility and efficiency are dual attributes of the organizational structure that are crucial for project-driven enterprises to achieve sustainable development in a dynamic environment. However, there is a lack of research on the patterns by which the dual attributes of a project-driven enterprise’s organizational structure affect business model innovation. Employing organizational theory, this study aims to assess the mediating mechanisms and dynamic capabilities through which the dual attributes of the organizational structure influence business model innovation in project-driven enterprises.

Data were collected from 242 employees from four project-driven companies across 26 cities (e.g. Beijing, Tianjin, Guangzhou and Shenzhen) in China. Structural equation modeling revealed the relationship between organizational structure’s dual attributes and business model innovation.

The findings show that the dual attributes (flexibility and efficiency) of the organizational structure have positive impacts on business model innovation. Moreover, dynamic capabilities mediate the relationship between the dual attributes and business model innovation in project-driven enterprises.

This study provides contributions to innovation research in the context of project-driven enterprises by revealing the influence of organizational structure on business model innovation through the firms’ dynamic capabilities. Such knowledge can enable managers of project-driven enterprises to develop effective interventions to promote business model innovation.

In this paper, the seismic behavior of the gusset plate moment connection (GPMC) exposed to the post-earthquake fire (PEF) is investigated.

For this purpose, for the sake of verification, first, a numerical model is built using ABAQUS software and then exposed to earthquakes and high temperatures. Afterward, the effects of a series of parameters, such as gusset plate thickness, gap width, steel grade, vertical load value and presence of the stiffeners, are evaluated on the behavior of the connection in the PEF conditions.

Based on the results obtained from the parametric study, all parameters effectively played a role against the seismic loads, although, when exposed to fire, it was found that the vertical load value and presence of the stiffener revealed a great contribution and the other parameters could not significantly affect the connection performance. Finally, to develop the modeling and further study the performance of the connection, the 4 and 8-story frames are subjected to 11 accelerograms and 3 different fire scenarios. The findings demonstrate that high temperatures impose rotations on the structure, such that the story drifts were changed compared to the post-earthquake drift values.

The obtained results can be used by engineers to design the GPMC for the combined action of earthquake and fire.

The present paper aims at the multi-objective optimization of a reentrant hexagonal cell auxetic structure. In addition, a parametric analysis will be carried out to verify how each of the design factors impact each of the responses.

The multi-objective optimization of five different responses of an auxetic model was considered: mass, critical buckling load under compression effort, natural frequency, Poisson's ratio and failure load. The response surface methodology was applied, and a new meta-heuristic of optimization called the multi-objective Lichtenberg algorithm was applied to find the optimized configuration of the model. It was possible to increase the failure load by 26.75% in compression performance optimization. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively. This paper addresses something new in the scientific world to date when evaluating in a multi-objective optimization problem, the compression and modal performance of an auxetic reentrant model.

It was possible to find multi-objective optimized structures. It was possible to increase the critical buckling load by 42.82%, and the failure load in compression performance by 26.75%. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%, and decrease the mass by 15.97%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, increase the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively.

There is no work in the literature to date that performed the optimization of 5 responses simultaneously of a reentrant hexagonal cell auxetic structure. This paper also presents an unprecedented statistical analysis in the literature that verifies how the design factors impact each of the responses.

This study aims to present the variations of optimal seismic control of reinforced cement concrete (RCC) structure using different structural systems. Different third-dimensional mathematical models are used to examine the responses of multistory flexibly connected frames subjected to earthquake excitations.

This paper examined a G + 50 multi-storied high-rise structure, which is analyzed using different combinations of moment resistant frames, shear walls, seismic outrigger systems and seismic dampers to observe the effectiveness during ground motion against soft soil conditions. The damping coefficients of added dampers, providing both upper and lower levels are taken into consideration. A finite element modeling and analysis is generated. Then the nature of the structure exposed to ground motion is captured with response spectrum analysis, using BNBC-2020 for four different seismic zones in Bangladesh.

The response of the structure is investigated according to the amplitude of the displacements, drifts, base shear, stiffness and torsion. The numerical results indicate that adding dampers at the base level can be the most effective against seismic control. However, placing an outrigger bracing system at the middle and top end with shear wall can be the most effective for controlling displacements and drifts.

The response of high-rise structures to seismic forces in Bangladesh’s soft soil conditions is examined at various levels in this study. This study is an original research which contributes to the knowledge to build earthquake resisting high-rises in Bangladesh.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.