Research is an extremely dynamic process. As time passes, certain areas of research become more salient, while others require less attention. As a researcher at Semnan University, my research in the last 15 years has been focused on four primary areas: Soft Computing (AI/Optimization), Structural Reliability, Seismic Resilience, and Damage Detection of Structures.

Structural Reliability attempts to answer the following questions: How can we measure the safety of structures? Safety can be measured in terms of reliability or the probability of uninterrupted operation. The complement to reliability is the probability of failure. How safe is safe enough? It is impossible to have an absolutely safe structure. Every structure has a certain nonzero probability of failure. Conceptually, we can design the structure to reduce the probability of failure, but increasing the safety (or reducing the probability of failure) beyond a certain optimum level is not always economical. This optimum safety level has to be determined. How does a designer implement the optimum safety level? Once the optimum safety level is determined, appropriate design provisions must be established so that structures will be designed accordingly. Implementation of the target reliability can be accomplished through the development of probability-based design codes. Society expects buildings and bridges to be designed with a reasonable safety level. In practice, these expectations are achieved by following code requirements specifying design values for minimum strength, maximum allowable deflection, and so on. Code requirements have evolved to include design criteria that take into account some of the sources of uncertainty in design. Such criteria are often referred to as reliability-based design criteria. The reliability of a structure is its ability to fulfill its design purpose for some specified design lifetime. Reliability is often understood to equal the probability that a structure will not fail to perform its intended function. My research works on this area include reliability assessment of RC frames rehabilitated by eccentrically braces having vertical shear link, reliability assessment of shear-deficient reinforced concrete beams externally bonded by FRP sheets having different configurations, reliability assessment of shear model analytical uncertainty of ACI 440.2r-08 for FRP strengthening configurations of RC beams, structural safety assessment of FRP-retrofitted RC frames in terms of simulation techniques, probabilistic residual capacity assessment of main shock-damaged multi-span simply supported concrete girder bridges subjected to aftershocks, a reliability-based approach and code calibration of FRP-confined rectangular RC columns subjected to concentric loading, seismic assessment of RC structures having shape memory alloys rebar and strengthened using CFRP sheets in terms of fragility curves, safety assessment of dual shear wall-frame structures subject to mainshock-aftershock sequence in terms of fragility and vulnerability curves and evaluation of seismic reliability of steel moment-resisting frames rehabilitated by concentric braces with probabilistic models.

Resiliency of an urban area, housings, school, hospital, infrastructure, etc., is vital in the continuation of life after a disaster. Generally speaking, resilience can be related to sustaining the influence of extreme events and to recover the main performance and functionality efficiently. Resilience is defined as a function indicating the capability to sustain a level of functionality or performance for a given building, bridge, lifeline networks, or community, over a period defined as the control time that is usually decided by owners, or society. My research works on this area include seismic resilience evaluation based on vulnerability curves for existing and retrofitted typical RC school buildings, probabilistic evaluation of seismic resilience for typical vital buildings in terms of vulnerability curves, seismic resilience evaluation of reinforced concrete hospital buildings retrofitted using fluid viscous dampers, seismic resilience of hospital buildings having soft story: probabilistic structural vulnerability evaluation, seismic resilience evaluation of base-isolated RC buildings using a loss-recovery approach, and seismic resilience and residual functionality assessment of buildings using a probabilistic fuzzy approach.

Soft computing aims to surmount NP-complete problems, uses inexact methods to give useful but inexact answers to intractable problems, represents a significant paradigm shift in the aims of computing – a shift which reflects the human mind, is tolerant to imprecision, uncertainty, partial truth, and approximation and is well suited for real-world problems where ideal models are not available. The idea behind soft computing is to model the cognitive behavior of the human mind. Soft computing is the foundation of conceptual intelligence in machines. I am professional in all branches of soft computing, especially in Artificial Neural Networks, Fuzzy Sets, Neuro-Fuzzy Systems, and many evolutionary, heuristic and meta-heuristic optimization algorithms such as Genetic Algorithm, Ant Colony Optimization, Charged System Search, Artificial Bee Colony, Particle Swarm Algorithm, Cuckoo Search, Dolphin Echolocation, Glowworm Swarm Optimization, Big Bang-Big Crunch, Imperialist Competitive Algorithm, Bat Algorithm, Wolf Search, Krill Herd, Harmony Search, Stochastic Diffusion Search, Fish Swarm/School, and Monkey Search. During the last 15 years, I have applied soft computing techniques to many civil/structural engineering branches. I have developed bio-inspired and soft computing based predictive models for shear strength of reinforced concrete beams having steel stirrups, for moment capacity prediction of reinforced concrete columns, shear strength estimation of reinforced concrete beam–column sub‐assemblages, ultimate strength estimation of FRP-confined concrete cylinders, punching shear prediction of slab-column connections reinforced with FRP, moment capacity estimation of spirally reinforced concrete columns, failure modes in ductile and non-ductile concrete joints, torsional strength prediction of RC beams, bond strength modeling in FRP strip-to-concrete joints, compressive strength of FRP-confined circular reinforced concrete columns, moment capacity estimation of ferrocement members, axial strength estimation of non-compact and slender square CFT columns, evaluation of shear strength parameters of granulated waste rubber, compressive strength estimation of mortars having calcium inosilicate minerals, compressive strength prediction of environmentally friendly concrete, estimating the behavior of RC beams strengthened with NSM system, capacity prediction of FRP-strengthened RC joints, shear strength prediction of RC beams, estimation of project success, shear contribution of FRP in strengthened RC beams, prediction of critical distance between two MDOF systems subject to seismic excitation, compressive strength of mortars admixed with Wollastonite and Microsilica, torsional strength of reinforced concrete beams strengthened with FRP sheets, bond strength of composite rebars in concrete, evaluation of effective parameters on wave diffraction of far-fault ground motions,  analyzing soil-waste rubber shred mixtures, prediction of the capacity of CCFT short columns subject to short term axial load, analyzing the road rigid/flexible pavements, prediction of FRP-confined compressive strength of concrete.

Damage detection is a significant part of the structural health monitoring procedure, which tries to find the location and severity of damage in different structures. The classifications of SHM systems can be summarized as damage detection, damage localization, damage diagnosis, and finally, damage prognosis, which demonstrates how damage will develop and what will be the remaining life given the current damage state. There are many techniques for structural damage identification. The majority of the recently developed damage detection methods have focused on using vibrational properties of structures for localizing damage. The basis for these methods is the existing relationship between the vibrational parameters and the physical properties of a structure. The occurrence of damage causes some changes in the physical properties of structures which influence the vibrational properties. My investigations in the area of damage detection include damage detection of reinforced concrete frames subjected to subsequent ground motions using standardized decomposed details, structural damage detection of reinforced concrete shear walls subject to consequent earthquakes, a synthesis of peak picking method and wavelet packet transform for structural modal identification, detection of location and intensity due to multiple cracks in cantilever RC beam using modal analysis and wavelet transform, signal processing based damage detection of concrete bridge piers subjected to consequent excitations and damage severity quantification of steel structures in terms of WPT-PP method.

My main interest is the interdisciplinary fields of engineering. I believe that all branches of science and technology could be utilized in civil engineering to have efficient and precise methods of analysis, evaluation, and design. Soft computing and probabilistic methods can solve all problems in engineering depending upon their suitable utilization as well as proper knowledge and experience.