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In order to ensure the safety of existing buildings constructed many years ago in zones with high seismicity, it is very important to consider and apply retrofitting measures. The seismic retrofitting of buildings can be achieved by techniques such as increasing the stiffness and ductility of the building and reducing the seismic demand. Energy dissipative devices such as various types of dampers are among the most popular and widely studied devices for improving the performance of buildings exposed to earthquakes. This paper presents a systematic literature review of the seismic retrofitting of existing buildings using energy dissipating devices. More than 230 journal and conference articles were collected from three well-known scientific resources published from 2010 to 2024. The main classification of papers considered was based on energy-dissipating devices employed for retrofitting goals. According to this analysis, there is a vast number of energy dissipative devices and design methods studied by scholars, and energy dissipation based on friction, viscous, and hysteretic mechanisms are the most useful for dampers. On the other hand, only relatively few articles were found about seismic loss assessment and the economic aspects of buildings retrofitted with the proposed damping tools.

Base isolation is known as a useful and popular technique for seismic upgrading of reinforced concrete buildings. Predicting damage levels based on relative inter-story drift plays an important role for designing optimal base isolation systems. However, the existing codes usually rely on the acceleration spectrum for calculating the relative inter-story drift, and they do not provide an accurate estimation of the relative inter-story drift. Consequently, to cover the research gap, machine learning algorithms are being trained and used for identification of damage levels in retrofitted reinforced concrete buildings. More than 7000 datasets were derived by using nonlinear time-history and incremental dynamic analysis. A total of 48 reinforced concrete buildings with different stories and bay numbers were designed based on an older version of existing building codes, and then, base isolation systems were designed for the seismic retrofit. The machine learning algorithms used here were Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and an Artificial Neural Network. Based on the results, four of the mentioned algorithms have the capability of predicting the damage level with an accuracy of more than 85%, with the best performance being reached by extreme gradient boosting with an accuracy of 89%. Finally, the most important parameters affecting the damage levels of retrofitted reinforced concrete buildings were derived.

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration.

Maximizing benefit from budget allocation is a major challenge for municipalities in the modern era. This is especially significant when it comes to infrastructure network management such as water distribution networks. The main challenges of water distribution networks are leakage and leak repairs. Municipalities commonly use first-in-first-out approaches to determine which leaks to allocate budget for first. Yet, the deterioration of leaks is not linear through time and requires a more in-depth assessment of the condition of the leak. Therefore, this article presents two prioritization approaches for the scheduling of leaks while incorporating deterioration over time. This paper proposes and compares two optimization techniques: (1) a well-known genetic algorithm and (2) a novel approach named the Lazy Serpent Algorithm. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal order by using much less computation time. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.

After the sustainability of heritage buildings (HBs) has been evaluated and it is determined that rehabilitation is needed, then the life-cycle cost (LCC) of energy for HBs can be analyzed. The objective of this research was to evaluate the LCC of energy for HBs and develop a comprehensive life-cycle model that will aid in expenditure planning and budget allocation. This study was validated through the weighted sums method and two case studies—Murabba Palace (MP), Saudi Arabia; and Grey Nuns Building (GN), Canada—were evaluated. For any HB, the project life cycle includes planning, manufacturing, transportation, construction, operation, and maintenance phases. Saudi Arabian and Canadian experts completed questionnaires to attribute a percentage of importance of each of the aforementioned phases with respect to energy consumption. The findings from this study will enable facility managers to make effective rehabilitation decisions. The operation phase has the highest impact on the energy consumption, gas consumption, and cost of the building in both case studies. Moreover, the findings from this study can be used to establish cost-effective intervention strategies. Their responses were employed in the development of a life-cycle model. Excel® and Minitab® were used to calculate Cronbach’s alpha values for data reliability, and the project LCC of energy for the two case studies was studied. The operation phase appeared to be the most energy-consuming phase in both case studies, contributing the most to the cost of the building.

The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

Unlike steel reinforcements in concrete, Fiber Reinforced Polymer (FRP) materials are light and free from corrosion. Therefore, FRP materials are increasingly being used in structural engineering as a replacement for steel reinforcements. While the use of FRP bars as longitudinal reinforcements in concrete deep beams has been studied somewhat widely, their use and effectiveness as web reinforcements are not well studied. In this study, the effect of the FRP web reinforcements on the behaviour and strength of FRP-reinforced concrete deep beams were investigated in an experimental study. Four glass fiber-reinforced concrete (RC) simply supported deep beam specimens were tested under a concentrated load with different shear span-to-depth ratios and web reinforcement ratios. The behaviour of the deep beams was described in terms of load–deflection behaviour, crack developments, strain in FRP reinforcements, and failure modes. The experimental investigation emphasized the significance of web reinforcements in determining the reinforced concrete deep beam behaviour, such as mid-span deflection, crack breadth, failure modes, and ultimate strengths. Furthermore, to predict the behavior of deep beams, numerical Finite Element models using Abaqus software were created. The present test results were compared to those predicted using the Finite Element models. This investigation shows that web reinforcement is quite important for FRP-RC deep beams to achieve a robust behaviour by enhancing its capacity and deformability.

Saudi Arabia leads the Gulf Cooperation Council countries in the construction industry, with 63% of the region’s future projects and $1.4 trillion in building and transportation projects. Previous studies have shown that 60% of construction projects are behind the schedule. One of the reasons behind this delay is the inappropriate selection of contractors for construction projects; therefore, the development of a contractor performance assessment model is needed for construction projects in Saudi Arabia. The proposed model includes the major key performance indicators (KPIs) used to measure contractors’ performance in construction projects. Afterward, a questionnaire is conducted with construction professionals to recommend the important KPIs to consider while developing the model. The analytic hierarchy process decision-making technique is applied for assessing relative priorities among the identified KPIs based on expert or decision-maker consensus. The results show that the time category is the most important in selecting the best contractor, emphasizing the significance of timely project completion.

Fires pose significant risks, encompassing loss of life, destruction of property, and substantial adverse impacts on the economy. Therefore, the prioritization of fire safety in building structures must be embraced by all relevant stakeholders, including building owners, authorities, and the general public. However, traditional fire safety assessment methods can be laborious and challenging, impeding the identification of potential fire hazards and the selection of optimal fire safety measures. To this end, this research study offers an analytic hierarchy process for assessing building fire safety. Two case studies are presented to support this model’s outperformance compared with conventional assessment techniques. The proposed method yields hazard ratings of 5.3 and 4.3 along with safety ratings of 5.5 and 5.9 for the two case studies. Additionally, the proposed model yields comprehensible, well-documented, and comparable results. Therefore, it serves as a valuable decision-making tool for evaluating fire hazards and enhancing the efficiency of building structures. As a result, decision-makers can identify current and future fire protection and prevention requirements with greater ease and precision, making the decision-making process more effective.

Ground-penetrating radar (GPR) is an established technology with a wide range of applications for civil engineering, geological research, archaeological studies, and hydrological practices. In this regard, this study applies bibliometric and scientometric assessment to provide a systematic review of the literature on GPR-related research. This study reports the publication trends, sources of publications and subject categories, cooperation of countries, productivity of authors, citations of publications, and clusters of keywords in GPR-related research. The Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI), which can be accessed through the Web of Science Core Collection, are used as references. The findings report that the number of publications is 6880 between 2001 and 2021. The number of annual publications has increased significantly, from 139 in 2001 to 576 in 2021. The studies are published in 894 journals, and the annual number of active journals increased from 68 in 2001 to 215 in 2021. Throughout the study, the number of subject categories involved in GPR-related research fluctuated, ranging from 38 in 2001 to 68 in 2021. The research studies originated from 118 countries on 6 continents, where the United States and the People’s Republic of China led the research articles. The top five most common keywords are ground-penetrating radar, non-destructive testing, geophysics, electrical resistivity tomography, and radar. After investigating the clusters of keywords, it is determined that civil engineering, geological research, archaeological studies, and hydrological practices are the four main research fields incorporating GPR utilization. This study offers academics and practitioners an in-depth review of the latest research in GPR research as well as a multidisciplinary reference for future studies.