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This Study aims to present the seismic hazard assessment of the earthquake-prone eastern of Iran that has become more important due to its growing economic importance. Many cities in this region have experienced life and financial losses due to major earthquakes in recent years. Thus, in this study the seismic hazard maps and curves, and site-specific spectrums were obtained by using probabilistic approaches for the region.

The seismotectonic information, seismicity data and earthquake catalogues were gathered, main active seismic sources were identified and seismic zones were considered to cover the potential active seismic regions. The seismic model based on logic tree method used two seismic source models, two declustered catalogues, three choices for earthquake recurrence parameters and maximum considered earthquakes and four ground motion predicting (attenuation) models (GMPE).

The results showed a wide range of seismic hazards levels in the study region. The peak ground acceleration (PGAs) for 475 years returns period ranges between 0.1 g in the north-west part of the region with low seismic activity, to 0.52 g in the south-west part with high levels of seismicity. The PGAs for a 2,475-year period, also ranged from 0.12 to 0.80 g for the same regions. The computed hazard results were compared to the acceptable level of seismic hazard in the region based on Iran seismic code.

A new probabilistic approach has been developed for obtaining seismic hazard maps and curves; these results would help engineers in design of earthquake-resistant structures.

An interaction between emerging technologies (ETs) for facility management (FM) activities and stakeholder skills is necessary to promote the optimization of FM performance. Previous studies do not show strategies for the selection of ETs in FM considering the technological competencies of stakeholders. Thus, this study analyzes the interactions between ETs and FM from the perceptions of Brazilian professionals, identifying the most appropriate and effective technological solutions, based on a broad literature review.

The steps of the methodology are as follows: systematic literature review (SLR); detailing the ETs for FM; online questionnaire based on SLR findings; sample of Brazilian FM professionals; statistical treatment; and discussion.

Results indicate wireless sensor network, Internet of Thing, building information modeling and Big Data as ETs in FM with greater potential for optimization in the performance of FM activities, from survey respondents.

The scope of possible findings may have been biased, considering the small number of research participants and current transformations resulting from the COVID-19 pandemic (e.g. changes to standard operating procedures).

The results ensure greater security to facility managers in the effective implementation of ETs in FM activities.

The research explores the published studies and the consultation with Brazilian FM professionals in the selection of ETs.

Implementing building information modeling (BIM) in construction projects offers many benefits. However, the use of BIM in project cost management is still limited. This study aims to review the current trends in the application of BIM in project cost management.

This study systematically reviews the literature on the application of BIM in project cost management. A total of 46 related articles were identified and analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.

Eighteen approaches to applying BIM in project cost management were identified. The approaches can be grouped into cost control and cost estimation. Also, BIM can be applied independently or integrated with other techniques. The integrated approaches for cost control include integration with genetic algorithms, Monte Carlo simulation, lean construction, integrated project delivery, neural network and value engineering. On the contrary, integrated approaches for cost estimation include integration with cost-plus pricing, discrepancy analysis, construction progress curves, estimation standards, algorithms, declarative mappings, life cycle sustainability assessment, ontology, Web-based frameworks and structured query language.

To the best of the authors’ knowledge, this study is the first to systematically review prior literature on the application of BIM in project cost management. As a result, the study provides a comprehensive understanding of the current state of the art and fills the literature gap. Researchers and industry professionals can use the study findings to increase the benefits of implementing BIM in construction projects.

The main aim of this study is to examine the behavior of reinforced concrete short columns strengthened using longitudinal near surface mounted (NSM)-carbon fiber reinforced polymer (CFRP) strips.

A full 3D-finite element (FE) model was developed using ABAQUS in order to conduct the analysis. The model is first validated based on experimental data available in the literature, and then the effect of concrete compressive strength, number of CFRP strips that are used and the spacing between them were taken in consideration for both concentric and eccentric loading cases. The parametric study specimens were divided into three groups. The first group consisted of unstrengthened columns and served as control specimens. The second group consisted of columns strengthened by longitudinal CFRP strips at two opposite column faces.

The results of this study are used to develop interaction diagrams for CFRP-strengthened short columns and to develop best-fit equations to estimate the nominal axial load and moment capacities for these strengthened columns. The results showed that the specimens that were strengthened using more longitudinal CFRP strips showed a significant increase in axial load capacity and a significant improvement in the interaction diagram, especially at large load eccentricity values. This result can be justified by the fact that longitudinal strips effectively resist the bending moment that is generated due to eccentric loading. Generally, the process of strengthening using longitudinal strips only has a reasonable effect and it can be typically considered an excellent choice considering the economic aspect when the budget of strengthening is limited.

This research aims at studying the performance of strengthened rectangular reinforced concrete short columns with CFRP strips using FE method, developing interaction diagrams of strengthened columns in order to investigate the effect of different parameters such as compressive strength (20, 30 and 40 MPa), number of CFRP strips (1, 2, 3 and 4) and the spacing between CFRP strips in terms of the ratio of CFRP center point distance to column outside dimension ratio (0.60, 0.70 and 0.80) on the behavior of strengthened RC columns and improving empirical formulas to predict the nominal axial load and moment capacities of strengthened RC columns. These parameters that directly affect short column load carrying capacity are presented in ACI-318 (2014).

Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets.

The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used.

The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r² = 0.38) while decision tree (r² = 0.66) and gradient boosting (r² = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations.

The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.

The work on bridge resilience assessment includes quantitative and qualitative approaches to compare the multiple bridges based on their resilience. But still, the bridge resilience obtained by these assessment approaches is inefficient when prioritising multiple bridges to improve their resilience. Therefore, this study aims to develop a methodology for prioritising the bridges to improve their resilience.

The research methodology follows three sequential phases. In the first phase, criteria importance through intercriteria correlation (CRITIC) technique is used to compute the criteria weights. The criteria considered are age, area, design high flood level, finish road level FRL and resilience index of bridges. While 12 river-crossing bridges maintained by one bridge owner are considered as alternatives. Then, in the second phase, the prioritisation of each bridge is evaluated using five techniques, including technique for order of preference by similarity to ideal solution, VIKOR (in Serbian, Visekriterijumska Optimizacija I Kompromisno Resenje), additive ratio assessment, complex proportional assessment and multi-objective optimisation method by ratio analysis. Finally, in the third phase, the results of all five techniques are integrated using CRITIC and the weighted sum method.

The result of the study enables bridge owners to deal with the particular bridge that requires resilience improvement. The study concluded that it is not enough to consider only the bridge resilience index to improve its resilience. The prioritisation exercise should consider various other criteria that are not preferred during the bridge resilience assessment process.

The proposed methodology is a novel framework based on the existing multi-criteria decision-making (MCDM) techniques for contributing knowledge in the domain of bridge resilience management. It can efficiently overcome the pitfall of decision-making when two bridges have the same resilience index score.

Over the years, the significance of retrofitting has gained much attention with the unveiling of its different applications, such as energy retrofit and deep retrofit, to enhance the climate-resilience of buildings. However, no single study comprehensively assesses the climate-resilience of retrofitting. The purpose of this study is to address this gap via a systematic literature review.

Quality journal studies were selected using the PRISMA method and analysed manually and using scientometrics. Three dimensions of climate-resilience, such as robustness, withstanding and recovery, were used to evaluate the contribution of retrofit measures for achieving climate-resilient houses across four climate zones: tropical, arid, temperate and cold.

Most passive measures can enhance the robustness of residential buildings but cannot verify for withstanding against immediate shocks and timely recovery. However, some passive measures, such as night-time ventilation, show excellent performance over all four climate zones. Active measures such as heating, ventilation and air conditioning and mechanical ventilation with heat recovery, can ensure climate-resilience in all three dimensions in the short-term but contribute to greenhouse gas emissions, further exacerbating the long-term climate. Integrating renewable energy sources can defeat this issue. Thus, all three retrofit strategies should appropriately be adopted together to achieve climate-resilient houses.

Since the research is limited to secondary data, retrofit measures recommended in this research should be further investigated before application.

This review contributes to the knowledge domain of retrofitting by assessing the contribution of different retrofit measures to climate-resilience.

Prefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.

A model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.

The IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.

The sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

Nowadays, in building structures, dampers are connected to the building structure to reduce the damages caused by seismicity in addition to enhancing structural stability, and to connect dampers with the structure, joints are used. In this paper, three different configurations of double-lap joints were designed, developed and tested.

This paper aims to analyze three different categories of double-lap single-bolted joints that are used in connecting dampers with concrete and steel frame structures. These joints were designed and tested using computational, numerical and experimental methods. The studies were conducted to examine the reactions of the joints during loading conditions and to select the best joints for the structures that allow easy maintenance of the dampers and also withstand structural deformation when the damper is active during seismicity. Also, a computational analysis was performed on the designed joints integrated with the M25 concrete beam column junction. In this investigation, experimental study was carried out in addition to numerical and computational methods during cyclic load.

It was observed from the result that during deformation the double-base multiplate lap joint was suitable for buildings because the deformations on the joint base was negligible when compared with other joints. From the computational analysis, it was revealed that the three double joints while integrated with the beam column junction of M25 grade concrete structure, the damages induced by the double-base multiplate joint was negligible when compared with other two joints used in this study.

To prevent the collapse of the building during seismicity, dampers are used and further connecting the damper with the building structures, joints are used. In this paper, three double-lap joints in different design configuration were studied using computational, numerical and experimental techniques.

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.