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The damages in reinforced concrete (RC) beams due to reinforcement corrosion is a major problem in the RC industry. Accurate prediction of the residual bearing capacity of RC beams can effectively prevent structural failures or unwanted over-costs of inspections and rehabilitations. This paper proposes a novel machine learning-based prediction framework that combines the adaptive neural fuzzy inference system (ANFIS) with several metaheuristic algorithms for the effective estimation of the flexural strength capacity. Five optimization algorithms are employed for auto-selection of the optimum ANFIS parameters, including differential evolution (DE), genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm (FFA). A comprehensive experimental database of the flexural capacity of corroded steel reinforced concrete beams obtained from the literature, consisting of 177 tests, is used as a case study to evaluate the prediction performance of the proposed hybrid models. The results demonstrate that the proposed hybrid models transcend the previously developed models, while the optimized ANFIS using FFA represents the highest accuracy and strong stability among the proposed models. It is concluded that the proposed framework using ANFIS-FFA can be effectively employed as a useful tool for the accurate estimation of the flexural strength capacity of corroded reinforced concrete beams. [ABSTRACT FROM AUTHOR]

Waste glass (WG) can be used as fine aggregate and powder in concrete mixtures, preventing pollution induced by this non‐biodegradable material. The properties of WG‐included concrete should be examined before its practical use. Compressive strength (CS) is one of the most crucial characteristics of concrete, and the measurement of which needs time‐consuming and expensive experiments. The use of machine learning (ML) methods for modeling the CS of concrete can help achieve more reliable and precise models. In this study, a comprehensive database of WG‐included concrete was collected from the literature. Next, four ML methods, including support vector regression (SVR), least‐square support vector regression (LSSVR), adaptive neuro‐fuzzy inference system (ANFIS), and multilayer perceptron neural network (MLP) were served in the CS modeling. A recently proposed metaheuristic method, called marine predators algorithm (MPA), was proposed to optimize the control parameters of the ML models to guarantee generalized accuracy. Results indicate that the hybrid LSSVR‐MPA model outperforms the other developed ML models comparing the error metrics with an RMSE = 2.447 MPa and R2 = 0.983. The sensitivity analysis reveals that replacing the cement with WG powder decreases the CS, whereas serving the WG as the replacement for natural fine aggregate improves the CS. [ABSTRACT FROM AUTHOR]

Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges. [ABSTRACT FROM AUTHOR]

The capacity efficiency of load carrying with the accurate serviceability performances of reinforced concrete (RC) structure is an important aspect, which is mainly dependent on the values of the ultimate bond strength between the corroded steel reinforcements and the surrounding concrete. Therefore, the precise determination of the ultimate bond strength degradation is of paramount importance for maintaining the safety levels of RC structures affected by corrosion. In this regard, hybrid intelligence and machine learning techniques are proposed to build a new framework to predict the ultimate bond strength in between the corroded steel reinforcements and the surrounding concrete. The proposed computational techniques include the multilayer perceptron (MLP), the radial basis function neural network and the genetic expression programming methods. In addition to that, the Levenberg–Marquardt (LM) deterministic approach and two meta-heuristic optimization approaches, namely the artificial bee colony algorithm and the particle swarm optimization algorithm, are employed in order to guarantee an optimum selection of the hyper-parameters of the proposed techniques. The latter were implemented based on an experimental published database consisted of 218 experimental tests, which cover various factors related to the ultimate bond strength, such as compressive strength of the concrete, concrete cover, the type steel, steel bar diameter, length of the bond and the level of corrosion. Based on their performance evaluation through several statistical assessment tools, the proposed models were shown to predict the ultimate bond strength accurately; outperforming the existing hybrid artificial intelligence models developed based on the same collected database. More precisely, the MLP-LM model was, by far, the best model with a determination coefficient (R2) as high as 0.97 and 0.96 for the training and the overall data, respectively. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop accurate novel frameworks for the estimation of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven techniques. Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM). Additionally, a total of 259 measurement samples of maximum pitting corrosion depth for pipelines located in different environments were extracted from the literature and used for developing the AI-models in terms of training and testing.Furthermore, an investigation was carried out on the relationship between the maximum pitting depths and several combinations of probable factors that induce the pitting growth process such as the pipeline age, and the surrounding environmental properties. The results of the proposed AI-frameworks were compared using various criteria. Thus, statistical, uncertainty and external validation analyses were utilized to compare the efficiency and accuracy of the proposed AI-models and to investigate the main contributing factors for accurate predictions of the maximum pitting depth in the oil and gas pipeline. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a new framework for the prediction of stress intensity factor (SIF) using newly developed hybrid artificial intelligence (AI) models. To do so, an adaptive neuro‐fuzzy inference system optimized by two meta‐heuristic algorithms as genetic algorithm (ANFIS‐GA) and particle swarm optimization (ANFIS‐PSO) is proposed. Moreover, a database composed of 150 SIF values obtained using the finite element method (FEM) calculations is used for training and validating the two proposed AI models. The efficiency and accuracy of the proposed AI models were investigated through several assessment criteria. Results showed the outperformance of the ANFIS‐PSO model for accurate prediction of SIF values with R2= 0.9913, root mean square error (RMSE) = 23.6 and mean absolute error (MAE) = 18.07, whereas both AI models indicate a robust performance in the presence of input variability. Overall, the performed study provides a hybrid AI framework that can serve as an efficient numerical tool for SIF prediction and analysis. [ABSTRACT FROM AUTHOR]

• Comprehensive scientometric and systematic reviews of corrosion protection methods for metallic pipelines are detailed. • 122 research articles are used to divide, classify, and discuss external and internal corrosion protection methods. • The functions, limitations, and requirements of corrosion protection methods are reported and discussed. • The purpose of this review is to assist researchers in filling research gaps and focusing on future directions. Metallic pipelines carrying water and/or oil/gas are exposed to deterioration, leaks/bursts, and failures due to corrosion. A suitable corrosion protection technique can prevent corrosion of these metallic pipelines, particularly in hostile environments, and corrosive soils. It can also reduce pipe deterioration, leaks/breaks, and failure, prolong service life, and improve the transportation process. Based on prior studies, this comprehensive review is regarded as an early attempt to cover both external and internal corrosion protection techniques for metallic pipelines in depth. The external corrosion protection techniques are classified into passive, active and hybrid corrosion protection techniques. The passive techniques include coatings, linings, barriers, material design, electrical isolation, inhibitors, and multi-passive techniques. Whereas active corrosion protection techniques include sacrificial anode and impressed current cathodic protections. Active and passive techniques are frequently combined to provide a more comprehensive corrosion protection system against newly discovered corrosion causes or coating degradations. On the other hand, internal corrosion protection techniques include internal coatings/linings/barriers, corrosion allowance, inhibitors/chemical treatments, dehydration, pigging, pipe material selection and flow control. The functions, merits, demerits, limitations/shortcomings, and requirements of corrosion protection techniques, as well as the various considerations that control their use have been covered and discussed. This comprehensive review will assist researchers, practitioners, and the industrial sector in prioritizing their policies in order not only to select the appropriate external and internal corrosion protection technique but also to fill current research gaps and focus on the future directions. [ABSTRACT FROM AUTHOR]

• An improved directional simulation is developed using meta -heuristic algorithms. • Harris Hawks Optimization is integrated to determine the sensitivity vector. • Efficiency of reliability analysis is conducted against several reliability methods. • Results indicate a strong adaptation for solving highly nonlinear functions. • The proposed method estimate the reliability index with low computational cost. In this paper, a new framework for accurate reliability analysis is proposed based on improving the directional simulation by using metaheuristic algorithms. Usually for highly nonlinear and complex performance functions, finding the unit vector direction requires very high calculations or impossible practically. Hence, the novel improved version incorporates the Harris Hawks Optimization algorithm, where the unit vector of direction is formulated as a constrained optimization problem and estimated using metaheuristic algorithms. Given that metaheuristic algorithms have been introduced to solve unconstrained problems, the penalty function method is used to convert the constrained problem into an unconstrained problem. The applicability of the proposed framework is firstly tested on five highly nonlinear benchmark functions and then applied to solve four high-dimensional engineering problems. The performance of six simulations-based reliability analysis methods and the first-order reliability method were compared with the proposed method. Besides the feasibility of other metaheuristic algorithms were investigated. The results show high-performance abilities of the improved version of the directional simulation for solving highly nonlinear engineering problems. [ABSTRACT FROM AUTHOR]

• Novel predictive models are developed for FRP- confined concrete. • Support Vector Regression is combined with meta -heuristic optimization algorithms. • The proposed hybrid models are compared with the existed design relations of the ultimate strain and strain capacities. • The proposed hybrid models showed superior abilities in terms of accuracy and agreement. Fibre Reinforced Polymer (FRP) composites can provide efficient enhancements in terms of strength and deformability for concrete structures, in which accurate predictions of FRP confined concrete ultimate conditions is highly essential to maintain safety levels, further structural analysis and members design. In this paper, three novel hybrid intelligence models were proposed based on the hybridization of Support Vector Regression (SVR) model with three bio-inspired optimization algorithms as genetic algorithm (GA), particle swarm optimization (PSO) and Whale optimization algorithm (WOA) for predicting the ultimate conditions of FRP-confined concrete. Moreover, 15 existing empirical relations for the prediction of the ultimate strength and strain of FRP-confined concrete have been comprehensively reviewed. The performances of the empirical models and the proposed hybrid models as SVR-GA, SVR-PSO and SVR-WOA are evaluated and compared based on a large database, including 780 circular FRP-confined concrete specimens, which are collected from the open-source published experiments. By comparing the predicted results based on several statistical indicators, the proposed hybrid SVR-models are generally outperforming the existing empirical relations in terms of accuracy and agreement with the experimental database. SVR-WOA provides superior performances than SVR-PSO, SVR-GA and all existed empirical models. The root mean square error is improved using SVR-WOA by 0.9%, 14.9 % and 37% for the ultimate strain capacity, and 2.7%, 4.6% and 17.3% for the ultimate strength compared to SVR-PSO, SVR-GA and the best empirical relation, respectively. [ABSTRACT FROM AUTHOR]

The estimation of the failure probability for complex systems is a crucial issue for sustainability. Reliability analysis methods are needed to be developed to provide accurate estimations of the safety levels for the complex systems and structures of today. In this paper, a novel hybrid framework for the reliability analysis of engineering systems and structures is extended to reduce the computational burden. The proposed hybrid framework is named as SVR–CFORM and consists of coupling two parts: the first is an enhanced first-order reliability method (FORM) using nonlinear conjugate map (CFORM); the second is an artificial intelligence technique called support vector regression (SVR). The conjugate FORM (CFORM) is adaptively formulated to improve the robustness of the original iterative FORM algorithm, whereas the SVR technique is used to enhance the efficiency of the reliability analysis by reducing the computational burden. The performance of the proposed SVR–CFORM formulation is compared in terms of efficiency and robustness with several FORM formulas (i.e. HL–RF, directional stability transformation method, conjugate HL–RF and finite step length) through different numerical/structural reliability examples. Results indicate that the proposed SVR–CFORM formulation is more accurate and efficient than other reliability methods. Based on the comparative analysis results, the SVR technique can highly reduce the computational costs and accurately model the response of complex performance functions, while the iterative CFORM formulation found to provide stable and robust reliability index results compared to the others reliability methods. • A novel FORM based reliability method is developed for robust analyses of nonlinear problems. • The conjugate FORM is combined with SVR as a robust and efficient MPP search method. • The proposed SVR–CFORM is compared with several reliability methods. • The low-computational cost with stable results is captured using SVR–CFORM compared to other FORM formulas. [ABSTRACT FROM AUTHOR]

Imperfections or uneven surfaces of welded areas due to uncertain errors such as equipment, weld quality, or mechanical damage may cause defects or anomalies such as corrosion or cracks and lead to other failures such as burst or leakage. Corrosion tends to occur at the uneven area due to the stagnation of the hydrocarbons for a long period, which directly can reduce the strength of the pipeline itself. This paper proposes a framework that incorporates structural reliability analysis (SRA) to analyze the safety and integrity levels of corroded girth welded API 5L X56 pipeline. By developing the limit state functions (LSFs) with reference to previous studies and existing codes and standards, the assessment was divided into two sections: assessing the pipeline as a single pipeline, and as sectional parts (girth welded area) of the pipeline. The probabilistic evaluation was carried out using Monte Carlo simulation (MCS), and the obtained results were compared in terms of the failure probability, Pf. The results demonstrated that two burst pressure models—the Shell-92 and Zhang et al. models—are highly conservative based on the failure probability, whereas the pipeline corrosion failure criterion (PCORRC) gives the most reasonable results for both general corroded and girth welded pipeline scenarios. The findings of this work also indicate that the failure probability in sectional pipes is more reliable than that in entire pipes due to the consideration of corrosion distributions in the pipeline. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) beams are basic elements used in the construction of various structures and infrastructural systems. When exposed to harsh environmental conditions, the integrity of RC beams could be compromised as a result of various deterioration mechanisms. One of the most common deterioration mechanisms is the formation of different types of corrosion in the steel reinforcements of the beams, which could impact the overall reliability of the beam. Existing classical reliability analysis methods have shown unstable results when used for the assessment of highly nonlinear problems, such as corroded RC beams. To that end, the main purpose of this paper is to explore the use of a structural reliability method for the multi-state assessment of corroded RC beams. To do so, an improved reliability method, namely the three-term conjugate map (TCM) based on the first order reliability method (FORM), is used. The application of the TCM method to identify the multi-state failure of RC beams is validated against various well-known structural reliability-based FORM formulations. The limit state function (LSF) for corroded RC beams is formulated in accordance with two corrosion types, namely uniform and pitting corrosion, and with consideration of brittle fracture due to the pit-to-crack transition probability. The time-dependent reliability analyses conducted in this study are also used to assess the influence of various parameters on the resulting failure probability of the corroded beams. The results show that the nominal bar diameter, corrosion initiation rate, and the external loads have an important influence on the safety of these structures. In addition, the proposed method is shown to outperform other reliability-based FORM formulations in predicting the level of reliability in RC beams. [ABSTRACT FROM AUTHOR]

• A probabilistic framework for the structural integrity and fracture is proposed. • Pipelines of mid- and high-strength steels under corrosion are analysed. • Novel burst pressure models are included in the reliability analysis. • The effect of different models for the strain-hardening exponent is suggested. • The probability analysis for separated- and multi-failure modes is conducted. In order to reduce the economic costs of pipeline construction projects and for offering a good combination of strength and toughness for efficient transportation of large quantities of hydrocarbon products under high pressure, High Strength Steels (HSS) such as API 5L X70 to X120 are used recently in the construction of pipeline systems for the large oil and gas projects. The commonly utilized models for the reliability evaluation of the HSS pipelines may lead to some conservatism regarding the used data. This paper aims to evaluate the system reliability of HSS pipelines with combined corrosion and cracks defects. Therefore, two failure modes as the plastic collapse and fracture are considered. The effect of different correlations under the term of the strain-hardening exponent that depends on the yield to ultimate tensile strength (Y/T) ratio is investigated. The reliability index of HSS pipelines is evaluated separately for each failure mode using the subset simulation technique. Herein, the tensile strength proprieties of the HSS pipelines are taken into consideration, while the applied methodology utilizes novel probabilistic models to predict the burst pressure for the plastic collapse failure mode. The steels toughness is taken as equal to the minimum requirement for both the ductile and the brittle fracture arrest applied in the HSS pipelines. Moreover, the reliability of the system with multiple failure modes is evaluated to show the mutual existence effect of crack and corrosion defects on pipeline safety. [ABSTRACT FROM AUTHOR]

The use of composite flexible pipelines seems reliable and promising to the oil and gas industry to transport hydrocarbons as it is one of the best alternatives to overcome the major problem of corrosion in conventional carbon steel pipelines. However, throughout the years of operation, composite flexible pipelines can also degrade and tend to experience failures such as cracking, delamination and buckling due to several external or internal factors. It is crucial to periodically inspect and monitor the actual condition of composite pipelines in order to determine their safety and integrity throughout their design life. Therefore, with the scientometric and systematic review method, this paper aims to provide insight and future direction on the reliability and integrity assessment of composite pipelines that may be beneficial to pipeline operators as well as researchers or scholars within this field of expertise. Discussion on the common failure drivers, modes and mechanisms are detailed to better understand the relationship between the failure, reliability and integrity of composite flexible pipelines. A brief discussion about the available inspection and monitoring techniques for composite pipelines is also reported. From this comprehensive review, it is found that available approaches for the structural reliability analysis and integrity assessments of composite flexible pipelines are mainly based on quantitative risk-based inspection (QRBI) to produce a more reliable risk interpretation. Nevertheless, combining multiple inspection techniques and employing machine learning methodologies can aid in improving the real-time damage evaluation and safety performance of composite flexible pipes. • A scientometric review of 184 peer-reviewed articles on composite pipes in the oil and gas industry is being done. • A thorough examination of failure drivers, failure modes, and failure mechanisms is provided. • The evaluation discusses inspection and monitoring methodologies, as well as safety procedures. • Research gab and future directions for dealing with them are also offered. [ABSTRACT FROM AUTHOR]

Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO–HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence. • Two optimization algorithms are proposed as novel hybrid FORM in structural reliability analysis. • Local adjusting process is proposed in hybrid FORM methods of EPSO and PSO–HS. • PSO–HS and EPSO compared with PSO, HS, IHS, IPSO, LS-PSO and six FORM algorithms. • Proposed methods are more efficient than FORM for high-dimensional problems. [ABSTRACT FROM AUTHOR]

• Integration of inertia and viscous forces with attack angle in dynamic impact pressure modelling is presented. • The significance of Hershel-Bulkley rheological model in CFD simulation is highlighted. • Effect of debris flow regime in impact mechanism is revealed. • The findings provide valuable insights of pipeline design and mitigation strategies in mountainous regions. The interaction of debris flows, and impacted structures is still not well understood due to the complex mechanisms of the flows. Although several methodologies from experimental to numerical have developed so far, a thorough assessment of impact mechanism necessitates multiphase modelling approaches. Therefore, this paper presents a dynamic impact pressure modelling approach of debris flows using experimental and numerical techniques. The experimental investigations involved seven type of debris flows based on solid volume fraction (α s), that being released on an inclined flume, impacting a 2″(52 mm) pipe model. The numerical investigations were performed in Computational Fluid Dynamics (CFD) environment by utilising the Spalart-Allmaras turbulence and Hershel- Bulkley rheological models by considering different inclined angle (β) impact scenarios. The findings revealed that all debris flows produced were in supercritical flows regime and characterized as dilute, medium viscous, and highly viscous flows, based on Froude (Fr) and Reynold (Re) numbers. Drag coefficients ( C D - 90 / C D - 0 ) were behaved non-linearly with respect to Fr and Re. The proposed refined dynamic normal ( p d - 90 ) and axial (p d - 0 ) impact models using different set of Fr , Re and β will offer valuable insight for pipeline operators seeking to comprehend the complex interaction between debris flows and pipelines. [ABSTRACT FROM AUTHOR]

• Experimental burst capacity are conducted using API 5L X42 pipe specimens. • Numerical modelling based FEM is used for various radial arrangements. • The influence of the radial effect is investigated experimentally and numerically. • Radial interacting rules are developed for several arrangements of defect colonies. Pipelines failures during its service life caused by the impact of multiple corrosion defects are most probable to occur, which may induce severe consequences as compared to single defect acting alone. Interaction rules were developed for multiple defects in the longitudinal and circumferential directions previously, but the knowledge on two or more defects aligned radially is still not well understood. This paper aims at investigating new interacting limits when corrosion defects are acting both on the internal and external wall, separated by a radial spacing, S R. The studies were carried out using burst test experiments as well as numerical modelling using pipe specimens of type API 5L X42. Six colonies of radial interacting corrosion defect arrangements were tested. The paper investigates the effects of the radial interaction by means of radial limit, S R Lim . It was found that increasing the defect depths of internal and external defects would highly reduce the failure pressure, P multiple. Depending on the orientations of the multiple defects acting radially, the failure pressure may be less or more than the case of single defect, P single. Thus, the failure pressure ratio of P multiple /P single should be used as a measure, with any values larger than 0.99 depict no interaction taken place among those multiple defects. From the six colonies, only two colonies showed the significance of radial interactions. The paper then suggests that when radial interaction does not presence, multiple corrosion defects acting radially may then be treated as single defect. [ABSTRACT FROM AUTHOR]

• Experimental burst test is used to study the physical behavior of RTPs subjected to crack defects. • Numerical simulation based finite element analysis is carried out and adjusted using the burst test results. • Two crack orientations as longitudinal and circumferential are investigated. • Von-Mises stress criterion is implemented to compare the performance intact RTP, while Stress Intensity Factor for the cracked RTP. Reinforced Thermoplastic Pipe (RTP) is a composite pipeline that has received considerable attention in the oil and gas applications due to benefits such as high strength and corrosion resistance. Limitations occurred, however, when the performance of this pipe-type towards various defects is still not fully addressed. Thus, this paper attempts to highlight the response of RTP imposed to crack failures using hydrostatic burst test and validated with numerical simulations under various internal pressures. Two defect orientations were studied, namely longitudinal and circumferential, as measured with respect to the pipeline length. The established Von-Mises stress criterion was used to compare the intact RTP performance, while Stress Intensity Factor (SIF) for the cracked RTP. Parametric studies having different crack dimensions were also investigated, with results showing that the longitudinal crack would be giving higher impact as compared to the circumferential one, even at low operating pressure. Observations on the RTP specimen's failure were also shared. Results obtained from this study would be beneficial in giving initial understandings to pipeline operators on the RTP behaviors towards crack failures. [ABSTRACT FROM AUTHOR]

• An experimental program is developed to assess the RTP behavior under lateral load impact. • Dents are fabricated using a quasi-static indentation procedure to burst the RTP specimens. • Relationships between the lateral load, indentation depth and the burst pressure is extracted. Reinforced Thermoplastic Pipe (RTP) is a flexible composite pipe that currently receives fair acceptance in the oil and gas industry, with high potentials to be used as a replacement to the carbon steel (CS) pipes especially in high corrosive environment. As RTP is relatively new products, their designs and qualifications are governed by the industry practice at present in the absence of comprehensive technical design standards. Most literatures to date are mainly dedicated to the intact RTPs, while the response of the pipe towards various defects have not been fully understood. Quite often, the assessments are made by referring to the design standards of the CS pipes, which are not entirely suitable due to the flexible characteristics of the composite pipe itself. Thus, this paper aims at assessing the performance of a 4″ RTP when exposed to lateral loads from external interferences such as the anchor dropped. Herein, the damaged to the RTP pipe was made in the form of a dent indented by a cylindrical conical nose under the quasi-static indentation procedures carried out experimentally. The damaged or dented pipe was later tested for its burst capacity using a burst testing facility specially fabricated for composite pipes. During the experiments, two types of failure modes were observed, namely single unit and ply-by-ply failures. Outcomes obtained from the experiments were used to describe the relationships between three important parameters, namely lateral load, indentation depth and burst pressure using polynomial regression equations. These simplified equations may be adopted directly in pipeline assessments and operations of RTP pipes exposed to dents. Moreover, outcomes from this paper will be beneficial in qualifying further RTP as a new free-corrosion risk transportation system for the oil and gas industry. [ABSTRACT FROM AUTHOR]